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A unified learning algorithm to extract principal and minor components

journal contribution
posted on 2009-07-01, 00:00 authored by D Peng, Z Yi, Yong XiangYong Xiang
Recently, many unified learning algorithms have been developed to solve the task of principal component analysis (PCA) and minor component analysis (MCA). These unified algorithms can be used to extract principal component and if altered simply by the sign, it can also serve as a minor component extractor. This is of practical significance in the implementations of algorithms. Convergence of the existing unified algorithms is guaranteed only under the condition that the learning rates of algorithms approach zero, which is impractical in many practical applications. In this paper, we propose a unified PCA & MCA algorithm with a constant learning rate, and derive the sufficient conditions to guarantee convergence via analyzing the discrete-time dynamics of the proposed algorithm. The achieved theoretical results lay a solid foundation for the applications of our proposed algorithm.

History

Journal

Digital signal processing

Volume

19

Issue

4

Pagination

640 - 649

Publisher

Academic Press

Location

Maryland Heights, Mo.

ISSN

1051-2004

eISSN

1095-4333

Language

eng

Publication classification

C1 Refereed article in a scholarly journal; C Journal article

Copyright notice

2009, Elsevier Inc.