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0899-7667
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1530-888X
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Neural Computation

May 1992, Vol. 4, No. 3, Pages 382-392
(doi: 10.1162/neco.1992.4.3.382)
© 1992 Massachusetts Institute of Technology
Computing the Karhunen-Loeve Expansion with a Parallel, Unsupervised Filter System
Article PDF (490.32 KB)
Abstract

We use the invariance principle and the principles of maximum information extraction and maximum signal concentration to design a parallel, linear filter system that learns the Karhunen-Loeve expansion of a process from examples. In this paper we prove that the learning rule based on these principles forces the system into stable states that are pure eigenfunctions of the input process.