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Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning

2010

Conference Paper

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We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time, and not all data modalities may be present at application time. Maximum covariance analysis, as a generalization of PCA, has many desirable properties, but its application to practical problems is limited by its need for perfectly paired data. We overcome this limitation by a latent variable approach that allows working with weakly paired data and is still able to efficiently process large datasets using standard numerical routines. The resulting weakly paired maximum covariance analysis often finds better representations than alternative methods, as we show in two exemplary tasks: texture discrimination and transfer learning.

Author(s): Lampert, CH. and Kroemer, O.
Book Title: Computer Vision – ECCV 2010
Journal: Computer vision - ECCV 2010: 11th European Conference on Computer Vision
Pages: 566-579
Year: 2010
Month: September
Day: 0
Editors: Daniilidis, K. , P. Maragos, N. Paragios
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/978-3-642-15552-9_41
Event Name: 11th European Conference on Computer Vision
Event Place: Heraklion, Greece

Address: Berlin, Germany
Digital: 0
ISBN: 978-3-642-15552-9
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{6639,
  title = {Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning},
  author = {Lampert, CH. and Kroemer, O.},
  journal = {Computer vision - ECCV 2010: 11th European Conference on Computer Vision},
  booktitle = {Computer Vision – ECCV 2010},
  pages = {566-579},
  editors = {Daniilidis, K. , P. Maragos, N. Paragios},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2010},
  month_numeric = {9}
}