A framework for eigen and singular component analysis

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

Abstract

A framework that involves an unconstrained optimization of a polynomial type cost function weighted with a diagonal matrix is utilized to develop learning rules for principal and minor component analyzers. With some modifications, this cost function is also used to derive generalized principal and minor component analyzers, and principal singular component analyzers. Global and asymptotic stability of the proposed systems are analyzed via Liapunov theory and the Lasalle invariance principle.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 American Control Conference, ACC
Pages1654-1659
Number of pages6
DOIs
StatePublished - Dec 1 2007
Event2007 American Control Conference, ACC - New York, NY, United States
Duration: Jul 9 2007Jul 13 2007

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2007 American Control Conference, ACC
CountryUnited States
CityNew York, NY
Period7/9/077/13/07

Keywords

  • Asymptotic stability
  • Dynamical system
  • Generalized PCA
  • Global convergence
  • Global stability
  • Invariant set
  • Lasalle invariance principle
  • MCA
  • PCA
  • PSCA
  • PSSA
  • Principal singular flow
  • SVD
  • Unconstrained optimization

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