Separable PCA for image classification

Yongxin Taylor Xi, Peter J. Ramadge

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

5 Scopus citations

Abstract

As an alternative to standard PCA, matrix-based image dimensionality reduction methods have recently been proposed and have gained attention due to reported computational ef-ficiency and robust performance in classification. We unify all of these methods through one concept: Separable Principle Component Analysis (SPCA).We show that the proposed matrix methods are either equivalent to, special cases of, or approximations to SPCA. We include performance comparisons of the methods on two face data sets and a handwritten digit data set. The empirical results indicate that two existing methods, BD-PCA and its variant NGLRAM, are very good, efficiently computable, approximate solutions to practical SPCA problems.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages1805-1808
Number of pages4
DOIs
StatePublished - Sep 23 2009
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
Duration: Apr 19 2009Apr 24 2009

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
CountryTaiwan, Province of China
CityTaipei
Period4/19/094/24/09

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Discrete transforms
  • Eigenvalues and eigenfunctions
  • Face recognition
  • Image classification
  • Image representations

Fingerprint Dive into the research topics of 'Separable PCA for image classification'. Together they form a unique fingerprint.

Cite this