Simultaneous minor component extraction via weighted inverse Rayleigh quotient

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

Abstract

New criteria are proposed for extracting multiple minor components associated with the covariance matrix of an input process. The proposed minor component analysis (MCA) algorithms are 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. These algorithms may be seen as MCA counterparts of Oja's and Xu's systems for computing multiple principal component analysis. Simulation results to demonstrate algorithm performance are also presented.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesII561-II564
DOIs
StatePublished - Aug 6 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Inverse Rayleigh quotient
  • Minor component analysis
  • Oja's learning rule
  • Principal component analysis

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