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102 results (View BibTeX file of all listed publications)

2015

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Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism

Besserve, M.

53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

[BibTex]

2015

Besserve, M. Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism 53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

[BibTex]

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Independence of cause and mechanism in brain networks

Besserve, M.

DALI workshop on Networks: Processes and Causality, April 2015 (talk)

[BibTex]

Besserve, M. Independence of cause and mechanism in brain networks DALI workshop on Networks: Processes and Causality, April 2015 (talk)

[BibTex]

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Information-Theoretic Implications of Classical and Quantum Causal Structures

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.

18th Conference on Quantum Information Processing (QIP), 2015 (talk)

Web link (url) [BibTex]

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D. Information-Theoretic Implications of Classical and Quantum Causal Structures 18th Conference on Quantum Information Processing (QIP), 2015 (talk)

Web link (url) [BibTex]

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Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

[BibTex]

Castaneda, S. G., Katiyar, P., Russo, F., Disselhorst, J. A., Calaminus, C., Poli, S., Maurer, A., Ziemann, U., Pichler, B. J. Assessment of brain tissue damage in the Sub-Acute Stroke Region by Multiparametric Imaging using [89-Zr]-Desferal-EPO-PET/MRI World Molecular Imaging Conference, 2015 (talk)

[BibTex]

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Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J.

World Molecular Imaging Conference, 2015 (talk)

[BibTex]

Divine, M. R., Harant, M., Katiyar, P., Disselhorst, J. A., Bukala, D., Aidone, S., Siegemund, M., Pfizenmaier, K., Kontermann, R., Pichler, B. J. Early time point in vivo PET/MR is a promising biomarker for determining efficacy of a novel Db(\alphaEGFR)-scTRAIL fusion protein therapy in a colon cancer model World Molecular Imaging Conference, 2015 (talk)

[BibTex]

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Cosmology from Cosmic Shear with DES Science Verification Data

Abbott, T., Abdalla, F. B., Allam, S., Amara, A., Annis, J., Armstrong, R., Bacon, D., Banerji, M., Bauer, A. H., Baxter, E., others,

arXiv preprint arXiv:1507.05552, 2015 (techreport)

link (url) [BibTex]

Abbott, T., Abdalla, F. B., Allam, S., Amara, A., Annis, J., Armstrong, R., Bacon, D., Banerji, M., Bauer, A. H., Baxter, E., others, Cosmology from Cosmic Shear with DES Science Verification Data arXiv preprint arXiv:1507.05552, 2015 (techreport)

link (url) [BibTex]

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The DES Science Verification Weak Lensing Shear Catalogs

Jarvis, M., Sheldon, E., Zuntz, J., Kacprzak, T., Bridle, S. L., Amara, A., Armstrong, R., Becker, M. R., Bernstein, G. M., Bonnett, C., others,

arXiv preprint arXiv:1507.05603, 2015 (techreport)

link (url) [BibTex]

Jarvis, M., Sheldon, E., Zuntz, J., Kacprzak, T., Bridle, S. L., Amara, A., Armstrong, R., Becker, M. R., Bernstein, G. M., Bonnett, C., others, The DES Science Verification Weak Lensing Shear Catalogs arXiv preprint arXiv:1507.05603, 2015 (techreport)

link (url) [BibTex]

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The search for single exoplanet transits in the Kepler light curves

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

link (url) [BibTex]

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B. The search for single exoplanet transits in the Kepler light curves IAU General Assembly, 22, pages: 2258352, 2015 (talk)

link (url) [BibTex]

2011

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Combined whole-body PET/MR imaging: MR contrast agents do not affect the quantitative accuracy of PET following attenuation correction

Lois, C., Kupferschläger, J., Bezrukov, I., Schmidt, H., Werner, M., Mannheim, J., Pichler, B., Schwenzer, N., Beyer, T.

(SST15-05 ), 97th Scientific Assemble and Annual Meeting of the Radiological Society of North America (RSNA), December 2011 (talk)

Abstract
PURPOSE Combined PET/MR imaging entails the use of MR contrast agents (MRCA) as part of integrated protocols. We assess additional attenuation of the PET emission signals in the presence of oral and intraveneous (iv) MRCA made up of iron oxide and Gd-chelates, respectively. METHOD AND MATERIALS Phantom scans were performed on a clinical PET/CT (Biograph HiRez16, Siemens) and integrated whole-body PET/MR (Biograph mMR, Siemens) using oral (Lumirem) and intraveneous (Gadovist) MRCA. Reference PET attenuation values were determined on a small-animal PET (Inveon, Siemens) using standard PET transmission imaging (TX). Seven syringes of 5mL were filled with (a) Water, (b) Lumirem_100 (100% conc.), (c) Gadovist_100 (100%), (d) Gadovist_18 (18%), (e) Gadovist_02 (0.2%), (f) Imeron-400 CT iv-contrast (100%) and (g) Imeron-400 (2.4%). The same set of syringes was scanned on CT (Sensation16, Siemens) at 120kVp and 160mAs. The effect of MRCA on the attenuation of PET emission data was evaluated using a 20cm cylinder filled uniformly with [18F]-FDG (FDG) in water (BGD). Three 4.5cm diameter cylinders were inserted into the phantom: (C1) Teflon, (C2) Water+FDG (2:1) and (C3) Lumirem_100+FDG (2:1). Two 50mL syringes filled with Gadovist_02+FDG (Sy1) and water+FDG (Sy2) were attached to the sides of (C1) to mimick the effects of iv-contrast in vessels near bone. Syringe-to-background activity ratio was 4-to-1. PET emission data were acquired for 10min each using the PET/CT and the PET/MR. Images were reconstructed using CT- and MR-based attenuation correction. RESULTS Mean linear PET attenuation (cm-1) on TX was (a) 0.098, (b) 0.098, (c) 0.300, (d) 0.134, (e) 0.095, (f) 0.397 and (g) 0.105. Corresponding CT attenuation (HU) was: (a) 5, (b) 14, (c) 3070, (d) 1040, (e) 13, (f) 3070 and (g) 347. Lumirem had little effect on PET attenuation with (C3) being 13% and 10% higher than (C2) on PET/CT and PET/MR, respectively. Gadovist_02 had even smaller effects with (Sy1) being 2.5% lower than (Sy2) on PET/CT and 1.2% higher than (Sy2) on PET/MR. CONCLUSION MRCA in high and clinically relevant concentrations have attenuation values similar to that of CT contrast and water, respectively. In clinical PET/MR scenarios MRCA are not expected to lead to significant attenuation of the PET emission signals.

Web [BibTex]

2011

Lois, C., Kupferschläger, J., Bezrukov, I., Schmidt, H., Werner, M., Mannheim, J., Pichler, B., Schwenzer, N., Beyer, T. Combined whole-body PET/MR imaging: MR contrast agents do not affect the quantitative accuracy of PET following attenuation correction (SST15-05 ), 97th Scientific Assemble and Annual Meeting of the Radiological Society of North America (RSNA), December 2011 (talk)

Web [BibTex]

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Cooperative Cuts: a new use of submodularity in image segmentation

Jegelka, S.

Second I.S.T. Austria Symposium on Computer Vision and Machine Learning, October 2011 (talk)

Web [BibTex]

Jegelka, S. Cooperative Cuts: a new use of submodularity in image segmentation Second I.S.T. Austria Symposium on Computer Vision and Machine Learning, October 2011 (talk)

Web [BibTex]

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Effect of MR Contrast Agents on Quantitative Accuracy of PET in Combined Whole-Body PET/MR Imaging

Lois, C., Bezrukov, I., Schmidt, H., Schwenzer, N., Werner, M., Pichler, B., Kupferschläger, J., Beyer, T.

2011(MIC3-3), 2011 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS-MIC), October 2011 (talk)

Abstract
Combined whole-body PET/MR systems are being tested in clinical practice today. Integrated imaging protocols entail the use of MR contrast agents (MRCA) that could bias PET attenuation correction. In this work, we assess the effect of MRCA in PET/MR imaging. We analyze the effect of oral and intravenous MRCA on PET activity after attenuation correction. We conclude that in clinical scenarios, MRCA are not expected to lead to significant attenuation of PET signals, and that attenuation maps are not biased after the ingestion of adequate oral contrasts.

Web [BibTex]

Lois, C., Bezrukov, I., Schmidt, H., Schwenzer, N., Werner, M., Pichler, B., Kupferschläger, J., Beyer, T. Effect of MR Contrast Agents on Quantitative Accuracy of PET in Combined Whole-Body PET/MR Imaging 2011(MIC3-3), 2011 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS-MIC), October 2011 (talk)

Web [BibTex]

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First Results on Patients and Phantoms of a Fully Integrated Clinical Whole-Body PET/MRI

Schmidt, H., Schwenzer, N., Bezrukov, I., Kolb, A., Mantlik, F., Kupferschläger, J., Lois, C., Sauter, A., Brendle, C., Pfannenberg, C., Pichler, B.

2011(J2-8), 2011 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS-MIC), October 2011 (talk)

Abstract
First clinical fully integrated whole-body PET/MR scanners are just entering the field. Here, we present studies toward quantification accuracy and variation within the PET field of view of small lesions from our BrainPET/MRI, a dedicated clinical brain scanner which was installed three years ago in Tbingen. Also, we present first results for patient and phantom scans of a fully integral whole-body PET/MRI, which was installed two months ago at our department. The quantification accuracy and homogeneity of the BrainPET-Insert (Siemens Medical Solutions, Germany) installed inside the magnet bore of a clinical 3T MRI scanner (Magnetom TIM Trio, Siemens Medical Solutions, Germany) was evaluated by using eight hollow spheres with inner diameters from 3.95 to 7.86 mm placed at different positions inside a homogeneous cylinder phantom with an 9:1 and 6:1 sphere to background ratio. The quantification accuracy for small lesions at different positions in the PET FoV shows a standard deviation of up to 11% and is acceptable for quantitative brain studies where the homogeneity of quantification on the entire FoV is essental. Image quality and resolution of the new Siemens whole-body PET/MR system (Biograph mMR, Siemens Medical Solutions, Germany) was evaluated according to the NEMA NU2 2007 protocol using a body phantom containing six spheres with inner diameter from 10 to 37 mm at sphere to background ratios of 8:1 and 4:1 and the F-18 point sources located at different positions inside the PET FoV, respectively. The evaluation of the whole-body PET/MR system reveals a good PET image quality and resolution comparable to state-of-the-art clinical PET/CT scanners. First images of patient studies carried out at the whole-body PET/MR are presented highlighting the potency of combined PET/MR imaging.

Web [BibTex]

Schmidt, H., Schwenzer, N., Bezrukov, I., Kolb, A., Mantlik, F., Kupferschläger, J., Lois, C., Sauter, A., Brendle, C., Pfannenberg, C., Pichler, B. First Results on Patients and Phantoms of a Fully Integrated Clinical Whole-Body PET/MRI 2011(J2-8), 2011 IEEE Nuclear Science Symposium, Medical Imaging Conference (NSS-MIC), October 2011 (talk)

Web [BibTex]

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Effect of MR contrast agents on quantitative accuracy of PET in combined whole-body PET/MR imaging

Lois, C., Kupferschläger, J., Bezrukov, I., Schmidt, H., Werner, M., Mannheim, J., Pichler, B., Schwenzer, N., Beyer, T.

(OP314), Annual Congress of the European Association of Nuclear Medicine (EANM), October 2011 (talk)

Abstract
PURPOSE:Combined PET/MR imaging entails the use of MR contrast agents (MRCA) as part of integrated protocols. MRCA are made up of iron oxide and Gd-chelates for oral and intravenous (iv) application, respectively. We assess additional attenuation of the PET emission signals in the presence of oral and iv MRCA.MATERIALS AND METHODS:Phantom scans were performed on a clinical PET/CT (Biograph HiRez16, Siemens) and an integrated whole-body PET/MR (Biograph mMR, Siemens). Two common MRCA were evaluated: Lumirem (oral) and Gadovist (iv).Reference PET attenuation values were determined on a dedicated small-animal PET (Inveon, Siemens) using equivalent standard PET transmission source imaging (TX). Seven syringes of 5mL were filled with (a) Water, (b) Lumirem_100 (100% concentration), (c) Gadovist_100 (100%), (d) Gadovist_18 (18%), (e) Gadovist_02 (0.2%), (f) Imeron-400 CT iv-contrast (100%) and (g) Imeron-400 (2.4%). The same set of syringes was scanned on CT (Sensation16, Siemens) at 120kVp and 160mAs.The effect of MRCA on the attenuation of PET emission data was evaluated using a 20cm cylinder filled uniformly with [18F]-FDG (FDG) in water (BGD). Three 4.5cm diameter cylinders were inserted into the phantom: (C1) Teflon, (C2) Water+FDG (2:1) and (C3) Lumirem_100+FDG (2:1). Two 50mL syringes filled with Gadovist_02+FDG (Sy1) and water+FDG (Sy2) were attached to the sides of (C1) to mimick the effects of iv-contrast in vessels near bone. Syringe-to-background activity ratio was 4-to-1.PET emission data were acquired for 10min each using the PET/CT and the PET/MR. Images were reconstructed using CT- and MR-based attenuation correction (AC). Since Teflon is not correctly identified on MR, PET(/MR) data were reconstructed using MR-AC and CT-AC.RESULTS:Mean linear PET attenuation (cm-1) on TX was (a) 0.098, (b) 0.098, (c) 0.300, (d) 0.134, (e) 0.095, (f) 0.397 and (g) 0.105. Corresponding CT attenuation (HU) was: (a) 5, (b) 14, (c) 3070, (d) 1040, (e) 13, (f) 3070 and (g) 347.Lumirem had little effect on PET attenuation with (C3) being 13%, 10% and 11% higher than (C2) on PET/CT, PET/MR with MR-AC, and PET/MR with CT-AC, respectively. Gadovist_02 had even smaller effects with (Sy1) being 2.5% lower, 1.2% higher, and 3.5% lower than (Sy2) on PET/CT, PET/MR with MR-AC and PET/MR with CT-AC, respectively.CONCLUSION:MRCA in high and clinically relevant concentrations have attenuation values similar to that of CT contrast and water, respectively. In clinical PET/MR scenarios MRCA are not expected to lead to significant attenuation of the PET emission signals.

Web [BibTex]

Lois, C., Kupferschläger, J., Bezrukov, I., Schmidt, H., Werner, M., Mannheim, J., Pichler, B., Schwenzer, N., Beyer, T. Effect of MR contrast agents on quantitative accuracy of PET in combined whole-body PET/MR imaging (OP314), Annual Congress of the European Association of Nuclear Medicine (EANM), October 2011 (talk)

Web [BibTex]

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Multi-parametric Tumor Characterization and Therapy Monitoring using Simultaneous PET/MRI: initial results for Lung Cancer and GvHD

Sauter, A., Schmidt, H., Gueckel, B., Brendle, C., Bezrukov, I., Mantlik, F., Kolb, A., Mueller, M., Reimold, M., Federmann, B., Hetzel, J., Claussen, C., Pfannenberg, C., Horger, M., Pichler, B., Schwenzer, N.

(T110), 2011 World Molecular Imaging Congress (WMIC), September 2011 (talk)

Abstract
Hybrid imaging modalities such as [18F]FDG-PET/CT are superior in staging of e.g. lung cancer disease compared with stand-alone modalities. Clinical PET/MRI systems are about to enter the field of hybrid imaging and offer potential advantages. One added value could be a deeper insight into the tumor metabolism and tumorigenesis due to the combination of PET and dedicated MR methods such as MRS and DWI. Additionally, therapy monitoring of diffucult to diagnose disease such as chronic sclerodermic GvHD (csGvHD) can potentially be improved by this combination. We have applied PET/MRI in 3 patients with lung cancer and 4 patients with csGvHD before and during therapy. All 3 patients had lung cancer confirmed by histology (2 adenocarcinoma, 1 carcinoid). First, a [18F]FDG-PET/CT was performed with the following parameters: injected dose 351.7±25.1 MBq, uptake time 59.0±2.6 min, 3 min/bed. Subsequently, patients were brought to the PET/MRI imaging facility. The whole-body PET/MRI Biograph mMR system comprises 56 detector cassettes with a 59.4 cm transaxial and 25.8 cm axial FoV. The MRI is a modified Verio system with a magnet bore of 60 cm. The following parameters for PET acquisition were applied: uptake time 121.3±2.3 min, 3 bed positions, 6 min/bed. T1w, T2w, and DWI MR images were recorded simultaneously for each bed. Acquired PET data were reconstructed with an iterative 3D OSEM algorithm using 3 iterations and 21 subsets, Gaussian filter of 3 mm. The 4 patients with GvHD were brought to the brainPET/MRI imaging facility 2:10h-2:28h after tracer injection. A 9 min brainPET-acquisition with simultaneous MRI of the lower extremities was accomplished. MRI examination included T1-weighted (pre and post gadolinium) and T2-weighted sequences. Attenuation correction was calculated based on manual bone segmentation and thresholds for soft tissue, fat and air. Soleus muscle (m), crural fascia (f1) and posterior crural intermuscular septum fascia (f2) were surrounded with ROIs based on the pre-treatment T1-weighted images and coregistered using IRW (Siemens). Fascia-to-muscle ratios for PET (f/m), T1 contrast uptake (T1_post-contrast_f-pre-contrast_f/post-contrast_m-pre-contrast_m) and T2 (T2_f/m) were calculated. Both patients with adenocarcinoma show a lower ADC value compared with the carcinoid patient suggesting a higher cellularity. This is also reflected in FDG-PET with higher SUV values. Our initial results reveal that PET/MRI can provide complementary information for a profound tumor characterization and therapy monitoring. The high soft tissue contrast provided by MRI is valuable for the assessment of the fascial inflammation. While in the first patient FDG and contrast uptake as well as edema, represented by T2 signals, decreased with ongoing therapy, all parameters remained comparatively stable in the second patient. Contrary to expectations, an increase in FDG uptake of patient 3 and 4 was accompanied by an increase of the T2 signals, but a decrease in contrast uptake. These initial results suggest that PET/MRI provides complementary information of the complex disease mechanisms in fibrosing disorders.

Web [BibTex]

Sauter, A., Schmidt, H., Gueckel, B., Brendle, C., Bezrukov, I., Mantlik, F., Kolb, A., Mueller, M., Reimold, M., Federmann, B., Hetzel, J., Claussen, C., Pfannenberg, C., Horger, M., Pichler, B., Schwenzer, N. Multi-parametric Tumor Characterization and Therapy Monitoring using Simultaneous PET/MRI: initial results for Lung Cancer and GvHD (T110), 2011 World Molecular Imaging Congress (WMIC), September 2011 (talk)

Web [BibTex]

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Statistical Image Analysis and Percolation Theory

Langovoy, M., Habeck, M., Schölkopf, B.

2011 Joint Statistical Meetings (JSM), August 2011 (talk)

Abstract
We develop a novel method for detection of signals and reconstruction of images in the presence of random noise. The method uses results from percolation theory. We specifically address the problem of detection of multiple objects of unknown shapes in the case of nonparametric noise. The noise density is unknown and can be heavy-tailed. The objects of interest have unknown varying intensities. No boundary shape constraints are imposed on the objects, only a set of weak bulk conditions is required. We view the object detection problem as hypothesis testing for discrete statistical inverse problems. We present an algorithm that allows to detect greyscale objects of various shapes in noisy images. We prove results on consistency and algorithmic complexity of our procedures. Applications to cryo-electron microscopy are presented.

Web [BibTex]

Langovoy, M., Habeck, M., Schölkopf, B. Statistical Image Analysis and Percolation Theory 2011 Joint Statistical Meetings (JSM), August 2011 (talk)

Web [BibTex]

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PAC-Bayesian Analysis of Martingales and Multiarmed Bandits

Seldin, Y., Laviolette, F., Shawe-Taylor, J., Peters, J., Auer, P.

Max Planck Institute for Biological Cybernetics, Tübingen, Germany, May 2011 (techreport)

Abstract
We present two alternative ways to apply PAC-Bayesian analysis to sequences of dependent random variables. The first is based on a new lemma that enables to bound expectations of convex functions of certain dependent random variables by expectations of the same functions of independent Bernoulli random variables. This lemma provides an alternative tool to Hoeffding-Azuma inequality to bound concentration of martingale values. Our second approach is based on integration of Hoeffding-Azuma inequality with PAC-Bayesian analysis. We also introduce a way to apply PAC-Bayesian analysis in situation of limited feedback. We combine the new tools to derive PAC-Bayesian generalization and regret bounds for the multiarmed bandit problem. Although our regret bound is not yet as tight as state-of-the-art regret bounds based on other well-established techniques, our results significantly expand the range of potential applications of PAC-Bayesian analysis and introduce a new analysis tool to reinforcement learning and many other fields, where martingales and limited feedback are encountered.

PDF Web [BibTex]

Seldin, Y., Laviolette, F., Shawe-Taylor, J., Peters, J., Auer, P. PAC-Bayesian Analysis of Martingales and Multiarmed Bandits Max Planck Institute for Biological Cybernetics, Tübingen, Germany, May 2011 (techreport)

PDF Web [BibTex]

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Non-stationary Correction of Optical Aberrations

Schuler, C., Hirsch, M., Harmeling, S., Schölkopf, B.

(1), Max Planck Institute for Intelligent Systems, Tübingen, Germany, May 2011 (techreport)

Abstract
Taking a sharp photo at several megapixel resolution traditionally relies on high grade lenses. In this paper, we present an approach to alleviate image degradations caused by imperfect optics. We rely on a calibration step to encode the optical aberrations in a space-variant point spread function and obtain a corrected image by non-stationary deconvolution. By including the Bayer array in our image formation model, we can perform demosaicing as part of the deconvolution.

PDF [BibTex]

Schuler, C., Hirsch, M., Harmeling, S., Schölkopf, B. Non-stationary Correction of Optical Aberrations (1), Max Planck Institute for Intelligent Systems, Tübingen, Germany, May 2011 (techreport)

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Cooperative Cuts

Jegelka, S.

COSA Workshop: Combinatorial Optimization, Statistics, and Applications, March 2011 (talk)

Abstract
Combinatorial problems with submodular cost functions have recently drawn interest. In a standard combinatorial problem, the sum-of-weights cost is replaced by a submodular set function. The result is a powerful model that is though very hard. In this talk, I will introduce cooperative cuts, minimum cuts with submodular edge weights. I will outline methods to approximately solve this problem, and show an application in computer vision. If time permits, the talk will also sketch regret-minimizing online algorithms for submodular-cost combinatorial problems. This is joint work with Jeff Bilmes (University of Washington).

Web [BibTex]

Jegelka, S. Cooperative Cuts COSA Workshop: Combinatorial Optimization, Statistics, and Applications, March 2011 (talk)

Web [BibTex]

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Multiple Kernel Learning: A Unifying Probabilistic Viewpoint

Nickisch, H., Seeger, M.

Max Planck Institute for Biological Cybernetics, March 2011 (techreport)

Abstract
We present a probabilistic viewpoint to multiple kernel learning unifying well-known regularised risk approaches and recent advances in approximate Bayesian inference relaxations. The framework proposes a general objective function suitable for regression, robust regression and classification that is lower bound of the marginal likelihood and contains many regularised risk approaches as special cases. Furthermore, we derive an efficient and provably convergent optimisation algorithm.

Web [BibTex]

Nickisch, H., Seeger, M. Multiple Kernel Learning: A Unifying Probabilistic Viewpoint Max Planck Institute for Biological Cybernetics, March 2011 (techreport)

Web [BibTex]

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Multiple testing, uncertainty and realistic pictures

Langovoy, M., Wittich, O.

(2011-004), EURANDOM, Technische Universiteit Eindhoven, January 2011 (techreport)

Abstract
We study statistical detection of grayscale objects in noisy images. The object of interest is of unknown shape and has an unknown intensity, that can be varying over the object and can be negative. No boundary shape constraints are imposed on the object, only a weak bulk condition for the object's interior is required. We propose an algorithm that can be used to detect grayscale objects of unknown shapes in the presence of nonparametric noise of unknown level. Our algorithm is based on a nonparametric multiple testing procedure. We establish the limit of applicability of our method via an explicit, closed-form, non-asymptotic and nonparametric consistency bound. This bound is valid for a wide class of nonparametric noise distributions. We achieve this by proving an uncertainty principle for percolation on nite lattices.

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Langovoy, M., Wittich, O. Multiple testing, uncertainty and realistic pictures (2011-004), EURANDOM, Technische Universiteit Eindhoven, January 2011 (techreport)

PDF [BibTex]

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Nonconvex proximal splitting: batch and incremental algorithms

Sra, S.

(2), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2011 (techreport)

Abstract
Within the unmanageably large class of nonconvex optimization, we consider the rich subclass of nonsmooth problems having composite objectives (this includes the extensively studied convex, composite objective problems as a special case). For this subclass, we introduce a powerful, new framework that permits asymptotically non-vanishing perturbations. In particular, we develop perturbation-based batch and incremental (online like) nonconvex proximal splitting algorithms. To our knowledge, this is the rst time that such perturbation-based nonconvex splitting algorithms are being proposed and analyzed. While the main contribution of the paper is the theoretical framework, we complement our results by presenting some empirical results on matrix factorization.

PDF [BibTex]

Sra, S. Nonconvex proximal splitting: batch and incremental algorithms (2), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2011 (techreport)

PDF [BibTex]

2007

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Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism

Saigo, H., Hattori, M., Tsuda, K.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract
Secondary metabolic pathway in plant is important for finding druggable candidate enzymes. However, there are many enzymes whose functions are still undiscovered especially in organism-specific metabolic pathways. We propose reaction graph kernels for automatically assigning the EC numbers to unknown enzymatic reactions in a metabolic network. Experiments are carried out on KEGG/REACTION database and our method successfully predicted the first three digits of the EC number with 83% accuracy.We also exhaustively predicted missing enzymatic functions in the plant secondary metabolism pathways, and evaluated our results in biochemical validity.

Web [BibTex]

2007

Saigo, H., Hattori, M., Tsuda, K. Reaction graph kernels for discovering missing enzymes in the plant secondary metabolism NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Web [BibTex]

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Positional Oligomer Importance Matrices

Sonnenburg, S., Zien, A., Philips, P., Rätsch, G.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract
At the heart of many important bioinformatics problems, such as gene finding and function prediction, is the classification of biological sequences, above all of DNA and proteins. In many cases, the most accurate classifiers are obtained by training SVMs with complex sequence kernels, for instance for transcription starts or splice sites. However, an often criticized downside of SVMs with complex kernels is that it is very hard for humans to understand the learned decision rules and to derive biological insights from them. To close this gap, we introduce the concept of positional oligomer importance matrices (POIMs) and develop an efficient algorithm for their computation. We demonstrate how they overcome the limitations of sequence logos, and how they can be used to find relevant motifs for different biological phenomena in a straight-forward way. Note that the concept of POIMs is not limited to interpreting SVMs, but is applicable to general k−mer based scoring systems.

Web [BibTex]

Sonnenburg, S., Zien, A., Philips, P., Rätsch, G. Positional Oligomer Importance Matrices NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Web [BibTex]

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Machine Learning Algorithms for Polymorphism Detection

Schweikert, G., Zeller, G., Weigel, D., Schölkopf, B., Rätsch, G.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Web [BibTex]

Schweikert, G., Zeller, G., Weigel, D., Schölkopf, B., Rätsch, G. Machine Learning Algorithms for Polymorphism Detection NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Web [BibTex]

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An Automated Combination of Kernels for Predicting Protein Subcellular Localization

Zien, A., Ong, C.

NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Abstract
Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions.We propose a new class of protein sequence kernels which considers all motifs including motifs with gaps. This class of kernels allows the inclusion of pairwise amino acid distances into their computation. We utilize an extension of the multiclass support vector machine (SVM)method which directly solves protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. To automatically search over families of possible amino acid motifs, we optimize over multiple kernels at the same time. We compare our automated approach to four other predictors on three different datasets, and show that we perform better than the current state of the art. Furthermore, our method provides some insights as to which features are most useful for determining subcellular localization, which are in agreement with biological reasoning.

Web [BibTex]

Zien, A., Ong, C. An Automated Combination of Kernels for Predicting Protein Subcellular Localization NIPS Workshop on Machine Learning in Computational Biology, December 2007 (talk)

Web [BibTex]

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Challenges in Brain-Computer Interface Development: Induction, Measurement, Decoding, Integration

Hill, NJ.

Invited keynote talk at the launch of BrainGain, the Dutch BCI research consortium, November 2007 (talk)

Abstract
I‘ll present a perspective on Brain-Computer Interface development from T{\"u}bingen. Some of the benefits promised by BCI technology lie in the near foreseeable future, and some further away. Our motivation is to make BCI technology feasible for the people who could benefit from what it has to offer soon: namely, people in the "completely locked-in" state. I‘ll mention some of the challenges of working with this user group, and explain the specific directions they have motivated us to take in developing experimental methods, algorithms, and software.

[BibTex]

Hill, NJ. Challenges in Brain-Computer Interface Development: Induction, Measurement, Decoding, Integration Invited keynote talk at the launch of BrainGain, the Dutch BCI research consortium, November 2007 (talk)

[BibTex]

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Policy Learning for Robotics

Peters, J.

14th International Conference on Neural Information Processing (ICONIP), November 2007 (talk)

Web [BibTex]

Peters, J. Policy Learning for Robotics 14th International Conference on Neural Information Processing (ICONIP), November 2007 (talk)

Web [BibTex]

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Hilbert Space Representations of Probability Distributions

Gretton, A.

2nd Workshop on Machine Learning and Optimization at the ISM, October 2007 (talk)

Abstract
Many problems in unsupervised learning require the analysis of features of probability distributions. At the most fundamental level, we might wish to determine whether two distributions are the same, based on samples from each - this is known as the two-sample or homogeneity problem. We use kernel methods to address this problem, by mapping probability distributions to elements in a reproducing kernel Hilbert space (RKHS). Given a sufficiently rich RKHS, these representations are unique: thus comparing feature space representations allows us to compare distributions without ambiguity. Applications include testing whether cancer subtypes are distinguishable on the basis of DNA microarray data, and whether low frequency oscillations measured at an electrode in the cortex have a different distribution during a neural spike. A more difficult problem is to discover whether two random variables drawn from a joint distribution are independent. It turns out that any dependence between pairs of random variables can be encoded in a cross-covariance operator between appropriate RKHS representations of the variables, and we may test independence by looking at a norm of the operator. We demonstrate this independence test by establishing dependence between an English text and its French translation, as opposed to French text on the same topic but otherwise unrelated. Finally, we show that this operator norm is itself a difference in feature means.

PDF Web [BibTex]

Gretton, A. Hilbert Space Representations of Probability Distributions 2nd Workshop on Machine Learning and Optimization at the ISM, October 2007 (talk)

PDF Web [BibTex]

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Regression with Intervals

Kashima, H., Yamazaki, K., Saigo, H., Inokuchi, A.

International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)

Web [BibTex]

Kashima, H., Yamazaki, K., Saigo, H., Inokuchi, A. Regression with Intervals International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)

Web [BibTex]

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Bayesian Estimators for Robins-Ritov’s Problem

Harmeling, S., Toussaint, M.

(EDI-INF-RR-1189), School of Informatics, University of Edinburgh, October 2007 (techreport)

Abstract
Bayesian or likelihood-based approaches to data analysis became very popular in the field of Machine Learning. However, there exist theoretical results which question the general applicability of such approaches; among those a result by Robins and Ritov which introduce a specific example for which they prove that a likelihood-based estimator will fail (i.e. it does for certain cases not converge to a true parameter estimate, even given infinite data). In this paper we consider various approaches to formulate likelihood-based estimators in this example, basically by considering various extensions of the presumed generative model of the data. We can derive estimators which are very similar to the classical Horvitz-Thompson and which also account for a priori knowledge of an observation probability function.

PDF [BibTex]

Harmeling, S., Toussaint, M. Bayesian Estimators for Robins-Ritov’s Problem (EDI-INF-RR-1189), School of Informatics, University of Edinburgh, October 2007 (techreport)

PDF [BibTex]

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MR-Based PET Attenuation Correction: Method and Validation

Hofmann, M., Steinke, F., Scheel, V., Brady, M., Schölkopf, B., Pichler, B.

Joint Molecular Imaging Conference, September 2007 (talk)

Abstract
PET/MR combines the high soft tissue contrast of Magnetic Resonance Imaging (MRI) and the functional information of Positron Emission Tomography (PET). For quantitative PET information, correction of tissue photon attenuation is mandatory. Usually in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating source, or from the CT scan in case of combined PET/CT. In the case of a PET/MR scanner, there is insufficient space for the rotating source and ideally one would want to calculate the attenuation map from the MR image instead. Since MR images provide information about proton density of the different tissue types, it is not trivial to use this data for PET attenuation correction. We present a method for predicting the PET attenuation map from a given the MR image, using a combination of atlas-registration and recognition of local patterns. Using "leave one out cross validation" we show on a database of 16 MR-CT image pairs that our method reliably allows estimating the CT image from the MR image. Subsequently, as in PET/CT, the PET attenuation map can be predicted from the CT image. On an additional dataset of MR/CT/PET triplets we quantitatively validate that our approach allows PET quantification with an error that is smaller than what would be clinically significant. We demonstrate our approach on T1-weighted human brain scans. However, the presented methods are more general and current research focuses on applying the established methods to human whole body PET/MRI applications.

PDF Web [BibTex]

Hofmann, M., Steinke, F., Scheel, V., Brady, M., Schölkopf, B., Pichler, B. MR-Based PET Attenuation Correction: Method and Validation Joint Molecular Imaging Conference, September 2007 (talk)

PDF Web [BibTex]

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Learning with Transformation Invariant Kernels

Walder, C., Chapelle, O.

(165), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2007 (techreport)

Abstract
Abstract. This paper considers kernels invariant to translation, rotation and dilation. We show that no non-trivial positive definite (p.d.) kernels exist which are radial and dilation invariant, only conditionally positive definite (c.p.d.) ones. Accordingly, we discuss the c.p.d. case and provide some novel analysis, including an elementary derivation of a c.p.d. representer theorem. On the practical side, we give a support vector machine (s.v.m.) algorithm for arbitrary c.p.d. kernels. For the thin-plate kernel this leads to a classifier with only one parameter (the amount of regularisation), which we demonstrate to be as effective as an s.v.m. with the Gaussian kernel, even though the Gaussian involves a second parameter (the length scale).

PDF [BibTex]

Walder, C., Chapelle, O. Learning with Transformation Invariant Kernels (165), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, September 2007 (techreport)

PDF [BibTex]

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Scalable Semidefinite Programming using Convex Perturbations

Kulis, B., Sra, S., Jegelka, S.

(TR-07-47), University of Texas, Austin, TX, USA, September 2007 (techreport)

Abstract
Several important machine learning problems can be modeled and solved via semidefinite programs. Often, researchers invoke off-the-shelf software for the associated optimization, which can be inappropriate for many applications due to computational and storage requirements. In this paper, we introduce the use of convex perturbations for semidefinite programs (SDPs). Using a particular perturbation function, we arrive at an algorithm for SDPs that has several advantages over existing techniques: a) it is simple, requiring only a few lines of MATLAB, b) it is a first-order method which makes it scalable, c) it can easily exploit the structure of a particular SDP to gain efficiency (e.g., when the constraint matrices are low-rank). We demonstrate on several machine learning applications that the proposed algorithm is effective in finding fast approximations to large-scale SDPs.

PDF [BibTex]

Kulis, B., Sra, S., Jegelka, S. Scalable Semidefinite Programming using Convex Perturbations (TR-07-47), University of Texas, Austin, TX, USA, September 2007 (techreport)

PDF [BibTex]

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Bayesian methods for NMR structure determination

Habeck, M.

29th Annual Discussion Meeting: Magnetic Resonance in Biophysical Chemistry, September 2007 (talk)

Web [BibTex]

Habeck, M. Bayesian methods for NMR structure determination 29th Annual Discussion Meeting: Magnetic Resonance in Biophysical Chemistry, September 2007 (talk)

Web [BibTex]

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Sparse Multiscale Gaussian Process Regression

Walder, C., Kim, K., Schölkopf, B.

(162), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2007 (techreport)

Abstract
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of its two inputs fixed. We generalise this for the case of Gaussian covariance function, by basing our computations on m Gaussian basis functions with arbitrary diagonal covariance matrices (or length scales). For a fixed number of basis functions and any given criteria, this additional flexibility permits approximations no worse and typically better than was previously possible. Although we focus on g.p. regression, the central idea is applicable to all kernel based algorithms, such as the support vector machine. We perform gradient based optimisation of the marginal likelihood, which costs O(m2n) time where n is the number of data points, and compare the method to various other sparse g.p. methods. Our approach outperforms the other methods, particularly for the case of very few basis functions, i.e. a very high sparsity ratio.

PDF [BibTex]

Walder, C., Kim, K., Schölkopf, B. Sparse Multiscale Gaussian Process Regression (162), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2007 (techreport)

PDF [BibTex]

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Efficient Subwindow Search for Object Localization

Blaschko, M., Hofmann, T., Lampert, C.

(164), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2007 (techreport)

Abstract
Recent years have seen huge advances in object recognition from images. Recognition rates beyond 95% are the rule rather than the exception on many datasets. However, most state-of-the-art methods can only decide if an object is present or not. They are not able to provide information on the object location or extent within in the image. We report on a simple yet powerful scheme that extends many existing recognition methods to also perform localization of object bounding boxes. This is achieved by maximizing the classification score over all possible subrectangles in the image. Despite the impression that this would be computationally intractable, we show that in many situations efficient algorithms exist which solve a generalized maximum subrectangle problem. We show how our method is applicable to a variety object detection frameworks and demonstrate its performance by applying it to the popular bag of visual words model, achieving competitive results on the PASCAL VOC 2006 dataset.

PDF [BibTex]

Blaschko, M., Hofmann, T., Lampert, C. Efficient Subwindow Search for Object Localization (164), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August 2007 (techreport)

PDF [BibTex]

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Thinking Out Loud: Research and Development of Brain Computer Interfaces

Hill, NJ.

Invited keynote talk at the Max Planck Society‘s PhDNet Workshop., July 2007 (talk)

Abstract
My principal interest is in applying machine-learning methods to the development of Brain-Computer Interfaces (BCI). This involves the classification of a user‘s intentions or mental states, or regression against some continuous intentional control signal, using brain signals obtained for example by EEG, ECoG or MEG. The long-term aim is to develop systems that a completely paralysed person (such as someone suffering from advanced Amyotrophic Lateral Sclerosis) could use to communicate. Such systems have the potential to improve the lives of many people who would be otherwise completely unable to communicate, but they are still very much in the research and development stages.

PDF [BibTex]

Hill, NJ. Thinking Out Loud: Research and Development of Brain Computer Interfaces Invited keynote talk at the Max Planck Society‘s PhDNet Workshop., July 2007 (talk)

PDF [BibTex]

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Dirichlet Process Mixtures of Factor Analysers

Görür, D., Rasmussen, C.

Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5), June 2007 (talk)

Abstract
Mixture of factor analysers (MFA) is a well-known model that combines the dimensionality reduction technique of Factor Analysis (FA) with mixture modeling. The key issue in MFA is deciding on the latent dimension and the number of mixture components to be used. The Bayesian treatment of MFA has been considered by Beal and Ghahramani (2000) using variational approximation and by Fokoué and Titterington (2003) using birth-and –death Markov chain Monte Carlo (MCMC). Here, we present the nonparametric MFA model utilizing a Dirichlet process (DP) prior on the component parameters (that is, the factor loading matrix and the mean vector of each component) and describe an MCMC scheme for inference. The clustering property of the DP provides automatic selection of the number of mixture components. The latent dimensionality of each component is inferred by automatic relevance determination (ARD). Identifying the action potentials of individual neurons from extracellular recordings, known as spike sorting, is a challenging clustering problem. We apply our model for clustering the waveforms recorded from the cortex of a macaque monkey.

Web [BibTex]

Görür, D., Rasmussen, C. Dirichlet Process Mixtures of Factor Analysers Fifth Workshop on Bayesian Inference in Stochastic Processes (BSP5), June 2007 (talk)

Web [BibTex]

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New BCI approaches: Selective Attention to Auditory and Tactile Stimulus Streams

Hill, N., Raths, C.

Invited talk at the PASCAL Workshop on Methods of Data Analysis in Computational Neuroscience and Brain Computer Interfaces, June 2007 (talk)

Abstract
When considering Brain-Computer Interface (BCI) development for patients in the most severely paralysed states, there is considerable motivation to move away from BCI systems based on either motor cortex activity, or on visual stimuli. Together these account for most of current BCI research. I present the results of our recent exploration of new auditory- and tactile-stimulus-driven BCIs. The talk includes a tutorial on the construction and interpretation of classifiers which extract spatio-temporal features from event-related potential data. The effects and implications of whitening are discussed, and preliminary results on the effectiveness of a low-rank constraint (Tomioka and Aihara 2007) are shown.

PDF Web [BibTex]

Hill, N., Raths, C. New BCI approaches: Selective Attention to Auditory and Tactile Stimulus Streams Invited talk at the PASCAL Workshop on Methods of Data Analysis in Computational Neuroscience and Brain Computer Interfaces, June 2007 (talk)

PDF Web [BibTex]

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Towards Motor Skill Learning in Robotics

Peters, J.

Interactive Robot Learning - RSS workshop, June 2007 (talk)

Web [BibTex]

Peters, J. Towards Motor Skill Learning in Robotics Interactive Robot Learning - RSS workshop, June 2007 (talk)

Web [BibTex]

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Transductive Support Vector Machines for Structured Variables

Zien, A., Brefeld, U., Scheffer, T.

International Conference on Machine Learning (ICML), June 2007 (talk)

Abstract
We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over all possible labelings of the unlabeled data. In order to scale transductive learning to structured variables, we transform the corresponding non-convex, combinatorial, constrained optimization problems into continuous, unconstrained optimization problems. The discrete optimization parameters are eliminated and the resulting differentiable problems can be optimized efficiently. We study the effectiveness of the generalized TSVM on multiclass classification and label-sequence learning problems empirically.

PDF PDF Web [BibTex]

Zien, A., Brefeld, U., Scheffer, T. Transductive Support Vector Machines for Structured Variables International Conference on Machine Learning (ICML), June 2007 (talk)

PDF PDF Web [BibTex]

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Impact of target-to-target interval on classification performance in the P300 speller

Martens, S., Hill, J., Farquhar, J., Schölkopf, B.

Scientific Meeting "Applied Neuroscience for Healthy Brain Function", May 2007 (talk)

PDF Web [BibTex]

Martens, S., Hill, J., Farquhar, J., Schölkopf, B. Impact of target-to-target interval on classification performance in the P300 speller Scientific Meeting "Applied Neuroscience for Healthy Brain Function", May 2007 (talk)

PDF Web [BibTex]

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Cluster Identification in Nearest-Neighbor Graphs

Maier, M., Hein, M., von Luxburg, U.

(163), Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany, May 2007 (techreport)

Abstract
Assume we are given a sample of points from some underlying distribution which contains several distinct clusters. Our goal is to construct a neighborhood graph on the sample points such that clusters are ``identified‘‘: that is, the subgraph induced by points from the same cluster is connected, while subgraphs corresponding to different clusters are not connected to each other. We derive bounds on the probability that cluster identification is successful, and use them to predict ``optimal‘‘ values of k for the mutual and symmetric k-nearest-neighbor graphs. We point out different properties of the mutual and symmetric nearest-neighbor graphs related to the cluster identification problem.

PDF [BibTex]

Maier, M., Hein, M., von Luxburg, U. Cluster Identification in Nearest-Neighbor Graphs (163), Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany, May 2007 (techreport)

PDF [BibTex]

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Benchmarking of Policy Gradient Methods

Peters, J.

ADPRL Workshop, April 2007 (talk)

[BibTex]

Peters, J. Benchmarking of Policy Gradient Methods ADPRL Workshop, April 2007 (talk)

[BibTex]

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Exploring model selection techniques for nonlinear dimensionality reduction

Harmeling, S.

(EDI-INF-RR-0960), School of Informatics, University of Edinburgh, March 2007 (techreport)

Abstract
Nonlinear dimensionality reduction (NLDR) methods have become useful tools for practitioners who are faced with the analysis of high-dimensional data. Of course, not all NLDR methods are equally applicable to a particular dataset at hand. Thus it would be useful to come up with model selection criteria that help to choose among different NLDR algorithms. This paper explores various approaches to this problem and evaluates them on controlled data sets. Comprehensive experiments will show that model selection scores based on stability are not useful, while scores based on Gaussian processes are helpful for the NLDR problem.

PDF Web [BibTex]

Harmeling, S. Exploring model selection techniques for nonlinear dimensionality reduction (EDI-INF-RR-0960), School of Informatics, University of Edinburgh, March 2007 (techreport)

PDF Web [BibTex]

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Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach

Chiappa, S., Barber, D.

(161), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, March 2007 (techreport)

Abstract
We describe two related models to cluster multidimensional time-series under the assumption of an underlying linear Gaussian dynamical process. In the first model, times-series are assigned to the same cluster when they show global similarity in their dynamics, while in the second model times-series are assigned to the same cluster when they show simultaneous similarity. Both models are based on Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models in order to (semi) automatically determine an appropriate number of components in the mixture, and to additionally bias the components to a parsimonious parameterization. The resulting models are formally intractable and to deal with this we describe a deterministic approximation based on a novel implementation of Variational Bayes.

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Chiappa, S., Barber, D. Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach (161), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, March 2007 (techreport)

PDF [BibTex]

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Modeling data using directional distributions: Part II

Sra, S., Jain, P., Dhillon, I.

(TR-07-05), University of Texas, Austin, TX, USA, February 2007 (techreport)

Abstract
High-dimensional data is central to most data mining applications, and only recently has it been modeled via directional distributions. In [Banerjee et al., 2003] the authors introduced the use of the von Mises-Fisher (vMF) distribution for modeling high-dimensional directional data, particularly for text and gene expression analysis. The vMF distribution is one of the simplest directional distributions. TheWatson, Bingham, and Fisher-Bingham distributions provide distri- butions with an increasing number of parameters and thereby commensurately increased modeling power. This report provides a followup study to the initial development in [Banerjee et al., 2003] by presenting Expectation Maximization (EM) procedures for estimating parameters of a mixture of Watson (moW) distributions. The numerical challenges associated with parameter estimation for both of these distributions are significantly more difficult than for the vMF distribution. We develop new numerical approximations for estimating the parameters permitting us to model real- life data more accurately. Our experimental results establish that for certain data sets improved modeling power translates into better results.

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Sra, S., Jain, P., Dhillon, I. Modeling data using directional distributions: Part II (TR-07-05), University of Texas, Austin, TX, USA, February 2007 (techreport)

PDF [BibTex]

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Automatic 3D Face Reconstruction from Single Images or Video

Breuer, P., Kim, K., Kienzle, W., Blanz, V., Schölkopf, B.

(160), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, February 2007 (techreport)

Abstract
This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support Vector Machines (SVMs) and a Morphable Model of 3D faces. After SVM face detection, individual facial features are detected using a novel regression-and classification-based approach, and probabilistically plausible configurations of features are selected to produce a list of candidates for several facial feature positions. In the next step, the configurations of feature points are evaluated using a novel criterion that is based on a Morphable Model and a combination of linear projections. Finally, the feature points initialize a model-fitting procedure of the Morphable Model. The result is a high-resolution 3D surface model.

PDF [BibTex]

Breuer, P., Kim, K., Kienzle, W., Blanz, V., Schölkopf, B. Automatic 3D Face Reconstruction from Single Images or Video (160), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, February 2007 (techreport)

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