Learning kernel subspace classifier

Bailing Zhang, Hanseok Ko, Yongsheng Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Subspace classifiers are well-known in pattern recognition, which represent pattern classes by linear subspaces spanned by the class specific basis vectors through simple mathematical operations like SVD. Recently, kernel based subspace methods have been proposed to extend the functionalities by directly applying the Kernel Principal Component Analysis (KPCA). The projection variance in kernel space as applied in these earlier proposed kernel subspace methods, however, is not a trustworthy criteria for class discrimination and they simply fail in many recognition problems as we encountered in biometrics research. We address this issue by proposing a learning kernel subspace classifier which attempts to reconstruct data in input space through the kernel subspace projection. While the pre-image methods aiming at finding an approximate pre-image for each input by minimization of the reconstruction error in kernel space, we emphasize the problem of how to estimate a kernel subspace as a model for a specific class. Using the occluded face recognition as examples, our experimental results demonstrated the efficiency of the proposed method.

Original languageEnglish
Title of host publicationAdvances in Biometrics - International Conference, ICB 2007, Proceedings
PublisherSpringer Verlag
Pages299-308
Number of pages10
ISBN (Print)9783540745488
DOIs
Publication statusPublished - 2007
Event2007 International Conference on Advances in Biometrics, ICB 2007 - Seoul, Korea, Republic of
Duration: 2007 Aug 272007 Aug 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4642 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2007 International Conference on Advances in Biometrics, ICB 2007
CountryKorea, Republic of
CitySeoul
Period07/8/2707/8/29

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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