A class of learning algorithms for principal component analysis based on time-oriented hierarchical method

Authors:
Marko V. Jankovic
Affiliations:
Control Department, Institute of Electrical Engineering "Nikola Tesla", Belgrade, Serbia and Montenegro
Venue:
MUSP'06 Proceedings of the 6th WSEAS international conference on Multimedia systems & signal processing
Year:
2006

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Abstract

This paper presents a class of algorithms for principal component analysis obtained by modification of a class of algorithms for principal subspace analysis (PSA) known as Plumbley's General Stochastic Approximation. Modification of the algorithms is based on Time-Oriented Hierarchical Method. The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behaviour" of all output neurons. On a slower time scale, output neurons will compete for fulfilment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors.