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论文类型：期刊论文

发表时间：2008-10-15

发表刊物：JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS

收录刊物：SCIE、EI、Scopus

卷号：220

期号：1-2

页面范围：548-558

ISSN号：0377-0427

关键字：independent component analysis; constrained independent component analysis; Lagrange multiplier method; fixed-point algorithm

摘要：Independent component analysis (ICA) aims to recover a set of unknown mutually independent components (ICs) from their observed mixtures without knowledge of the mixing coefficients. In the classical ICA model there exists ICs' indeterminacy on permutation and dilation. Constrained ICA is one of methods for solving this problem through introducing constraints into the classical ICA model. In this paper we first present a new constrained ICA model which composed of three pans: a maximum likelihood criterion as an objective function, statistical measures as inequality constraints and the normalization of demixing matrix as equality constraints. Next, we incorporate the new fixed-point (newFP) algorithm into this constrained ICA model to construct a new constrained fixed-point algorithm. Computation simulations on synthesized signals and speech signals demonstrate that this combination both can eliminate ICs' indeterminacy to a certain extent, and can provide better performance. Moreover, comparison results with the existing algorithm verify the efficiency of our new algorithm furthermore, and show that it is more simple to implement than the existing algorithm due to its advantage of not using the learning rate. Finally. this new algorithm is also applied for the real-world fetal ECG data. experiment results further indicate the efficiency of the new constrained fixed-point algorithm. (c) 2007 Published by Elsevier B.V.