Sanger's like systems for generalized principal and minor component analysis

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

3 Scopus citations

Abstract

In this paper generalizations of Sanger's learning rule for nondefinite matrices are explored. It is shown that the left and right principal components of any matrix can be computed so that these components upper triangulize the original matrix. We also modified the original Sanger's system to obtain new dynamical systems with a larger domain of attraction. Stability analysis for several Sanger's type systems for the standard and generalized principal, and minor component analyzers applied to nonsymmetric matrices is developed.

Original languageEnglish (US)
Title of host publication2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006
Pages425-429
Number of pages5
StatePublished - Dec 1 2006
Event4th IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006 - Waltham, MA, United States
Duration: Jul 12 2006Jul 14 2006

Publication series

Name2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006

Other

Other4th IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006
CountryUnited States
CityWaltham, MA
Period7/12/067/14/06

Keywords

  • Dynamical systems
  • Generalized eigenvalue problem
  • Global stability
  • Minor component analysis
  • Oja's learning rule
  • Principal component analysis
  • Sanger's learning rule

Fingerprint Dive into the research topics of 'Sanger's like systems for generalized principal and minor component analysis'. Together they form a unique fingerprint.

  • Cite this

    Hasan, M. A. (2006). Sanger's like systems for generalized principal and minor component analysis. In 2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006 (pp. 425-429). (2006 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAM 2006).