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Learning to Find Pre-Images

2004

Conference Paper

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We consider the problem of reconstructing patterns from a feature map. Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of input points mapped into the RKHS. We introduce a technique based on kernel principal component analysis and regression to reconstruct corresponding patterns in the input space (aka pre-images) and review its performance in several applications requiring the construction of pre-images. The introduced technique avoids difficult and/or unstable numerical optimization, is easy to implement and, unlike previous methods, permits the computation of pre-images in discrete input spaces.

Author(s): Bakir, GH. and Weston, J. and Schölkopf, B.
Book Title: Advances in Neural Information Processing Systems 16
Journal: Advances in Neural Information Processing Systems
Pages: 449-456
Year: 2004
Month: June
Day: 0
Editors: S Thrun and LK Saul and B Sch{\"o}lkopf
Publisher: MIT Press

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

Event Name: 17th Annual Conference on Neural Information Processing Systems (NIPS 2003)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-20152-6
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2281,
  title = {Learning to Find Pre-Images},
  author = {Bakir, GH. and Weston, J. and Sch{\"o}lkopf, B.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 16},
  pages = {449-456},
  editors = {S Thrun and LK Saul and B Sch{\"o}lkopf},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2004},
  month_numeric = {6}
}