Time-Oriented Hierarchical Method for Computation of Minor Components

This paper proposes a general method that transforms known neural network MSA algorithms, into MCA algorithms. The method uses two distinct time scales. A given MSA algorithm is responsible, on a faster time scale, for the “behavior” of all output neurons. On this scale minor subspace is obtained. On a slower time scale, output neurons compete to fulfill their “own interests”. On this scale, basis vectors in the minor subspace are rotated toward the minor eigenvectors. Actually, time-oriented hierarchical method is proposed. Some simplified mathematical analysis, as well as simulation results are presented.

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