Neural networks for extracting Unsymmetric Principal Components

Sun-Yuan Kung, K. I. Diamantaras

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

6 Scopus citations

Abstract

In this paper we introduce two forms of Unsymmetric Principal Component Analysis (UPCA), namely the cross-correlation UPCA and the linear approximation UPCA problem. Both are concerned with the SVD of the input-teacher crosscorrelation matrix itself (first problem) or after prewhitening (second problem). The second problem is also equivalent to reduced-rank Wiener filtering. For the former problem, we propose an unsymmetric linear model for extracting one or more components using lateral inhibition connections in the hidden layer. The numerical convergence properties of the model are theoretically established. For the linear approximation UPCA problem, we can apply Back-Propagation extended either using a straightforward deflation procedure or with the use of lateral orthogonalizing connections in the hidden layer. All proposed models were tested and the simulation results confirm the theoretical expectations.

Original languageEnglish (US)
Title of host publicationNeural Networks for Signal Processing
PublisherPubl by IEEE
Pages50-59
Number of pages10
ISBN (Print)0780301188
StatePublished - Dec 1 1991
EventProceedings of the 1991 Workshop on Neural Networks for Signal Processing - NNSP-91 - Princeton, NJ, USA
Duration: Sep 30 1991Oct 2 1991

Publication series

NameNeural Networks for Signal Processing

Other

OtherProceedings of the 1991 Workshop on Neural Networks for Signal Processing - NNSP-91
CityPrinceton, NJ, USA
Period9/30/9110/2/91

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Kung, S-Y., & Diamantaras, K. I. (1991). Neural networks for extracting Unsymmetric Principal Components. In Neural Networks for Signal Processing (pp. 50-59). (Neural Networks for Signal Processing). Publ by IEEE.