Revisiting weighted inverse rayleigh quotient for minor component extraction

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

3 Scopus citations

Abstract

A framework for classes of minor component learning rides is presented. In the proposed rules, eigenvectors of a covariance matrix are simultaneously estimated. The derivation of MCA rules is based on optimizing a weighted inverse Rayleigh quotient so that the optimum weights at equilibrium points are exactly the desired eigenvectors of a covariance matrix instead of an arbitrary orthonormal basis of the minor subspace. Variations of the derived MCA learning rules are obtained by imposing orthogonal and quadratic constraints and change of variables. Some of the proposed algorithms can also perform PCA by merely changing the sign of the step-size.

Original languageEnglish (US)
Title of host publication2005 Fifth International Conference on Information, Communications and Signal Processing
Pages372-376
Number of pages5
StatePublished - Dec 1 2005
Event2005 Fifth International Conference on Information, Communications and Signal Processing - Bangkok, Thailand
Duration: Dec 6 2005Dec 9 2005

Publication series

Name2005 Fifth International Conference on Information, Communications and Signal Processing
Volume2005

Other

Other2005 Fifth International Conference on Information, Communications and Signal Processing
CountryThailand
CityBangkok
Period12/6/0512/9/05

Keywords

  • Adaptive learning algorithm
  • Extreme eigenvalues
  • Minor component analysis
  • Principal component analysis

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