Computing the Karhunen-Loeve Expansion with a Parallel, Unsupervised Filter System

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.