NDLI: Neural networks for extracting unsymmetric principal components

Content Provider	IEEE Xplore Digital Library
Author	Kung, S.Y. Diamantaras, K.I.
Copyright Year	1991
Description	Author affiliation: Dept. of Electr. Eng., Princeton Univ., NJ, USA (Kung, S.Y.; Diamantaras, K.I.)
Abstract	The authors 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 cross-correlation matrix itself (first problem) or after prewhitening (second problem). The second problem is also equivalent to reduced-rank Wiener filtering. For the former problem, the authors 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, one 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.
Starting Page	50
Ending Page	59
File Size	320679
Page Count	10
File Format	PDF
ISBN	0780301188
DOI	10.1109/NNSP.1991.239536
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1991-09-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Principal component analysis Linear approximation Vectors Wiener filter Data mining Autocorrelation Stochastic processes Convergence of numerical methods Testing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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