Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/54789
Title: A robust and globally convergent PCA learning algorithm
Authors: Ye, M.
Yi, Z.
Tan, K.K. 
Keywords: Eigenvector
Feature extraction
Neural network
Principal component analysis
Issue Date: 2007
Source: Ye, M.,Yi, Z.,Tan, K.K. (2007). A robust and globally convergent PCA learning algorithm. Control and Intelligent Systems 35 (2) : 119-125. ScholarBank@NUS Repository.
Abstract: Principal component analysis (PCA) using neural networks is an active research field with many applications to signal processing and data analysis. This paper presents a PCA neural network endowed with a novel learning algorithm, and an analysis of its features. As the basic discrete-time Oja's PCA neural network does not converge globally, it is important to derive a robust and globally convergent PCA learning algorithm. Based on previous works on the globally convergent PCA learning algorithm, a robust and globally convergent PCA learning algorithm is proposed in this paper. The behavior of this discrete-time learning algorithm is directly studied in this paper. We show that the algorithm is robust and globally convergent. The selection of the parameters of this algorithm will also be discussed in details. Finally, simulation results are provided to verify the theoretical results presented. Compared to other PCA learning algorithms, the proposed algorithm performs favorably in terms of robust stability, global convergence, speed and accuracy.
Source Title: Control and Intelligent Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/54789
ISSN: 14801752
Appears in Collections:Staff Publications

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