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Indexed by:期刊论文

Date of Publication:2008-10-01

Journal:NEUROCOMPUTING

Included Journals:SCIE、EI、Scopus

Volume:71

Issue:16-18,SI

Page Number:3656-3659

ISSN No.:0925-2312

Key Words:Independent component analysis; Blind source separation; Gaussian moments; Nonstationary variance; Autocorrelations

Abstract:A unifying model that combines three properties is proposed by Hyvarinen, and a gradient ascent algorithm for independent component analysis (ICA) is performed by maximum likelihood estimation. In this paper, we consider the estimation of the data model of ICA when Gaussian noise is present and the independent components are time dependent. Firstly, according to the useful property of Gaussian moments, we introduce Gaussian moments algorithm to estimation of the noisy unifying model when the noise covariance matrix is known. Next, when the noise covariance is unknown, a new Gaussian moments algorithm is developed. Finally, the validity and performance of our algorithms are demonstrated by computer simulations. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.