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Title: Adaptive multiple minor directions extraction in parallel using a PCA neural network
Authors: Tan, K.K. 
Lv, J.C.
Yi, Z.
Huang, S. 
Keywords: Deterministic discrete time (DDT) method
Global convergence
Minor component analysis (MCA)
Principal component analysis (PCA)
Issue Date: 1-Nov-2010
Citation: Tan, K.K., Lv, J.C., Yi, Z., Huang, S. (2010-11-01). Adaptive multiple minor directions extraction in parallel using a PCA neural network. Theoretical Computer Science 411 (48) : 4200-4215. ScholarBank@NUS Repository.
Abstract: A principal component analysis (PCA) neural network is developed for online extraction of the multiple minor directions of an input signal. The neural network can extract the multiple minor directions in parallel by computing the principal directions of the transformed input signal so that the stability-speed problem of directly computing the minor directions can be avoided to a certain extent. On the other hand, the learning algorithms for updating the net weights use constant learning rates. This overcomes the shortcoming of the learning rates approaching zero. In addition, the proposed algorithms are globally convergent so that it is very simple to choose the initial values of the learning parameters. This paper presents the convergence analysis of the proposed algorithms by studying the corresponding deterministic discrete time (DDT) equations. Rigorous mathematical proof is given to prove the global convergence. The theoretical results are further confirmed via simulations. © 2010 Elsevier B.V. All rights reserved.
Source Title: Theoretical Computer Science
ISSN: 03043975
DOI: 10.1016/j.tcs.2010.07.021
Appears in Collections:Staff Publications

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