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A Characteristic Function Based Contrast Function for Blind Extraction of Statistically Independent Signals Muhammad TUFAIL Masahide ABE Masayuki KAWAMATA Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E89-A No.8 pp.2149-2157 Publication Date: 2006/08/01 Online ISSN: 1745-1337 DOI: 10.1093/ietfec/e89-a.8.2149 Print ISSN: 0916-8508 Type of Manuscript: Special Section PAPER (Special Section on Papers Selected from the 20th Symposium on Signal Processing) Category: Keyword: independent component analysis, characteristic function, non-Gaussianity measure, contrast function, Full Text: PDF(362KB)>>
Summary: In this paper, we propose to employ a characteristic function based non-Gaussianity measure as a one-unit contrast function for independent component analysis. This non-Gaussianity measure is a weighted distance between the characteristic function of a random variable and a Gaussian characteristic function at some adequately chosen sample points. Independent component analysis of an observed random vector is performed by optimizing the above mentioned contrast function (for different units) using a fixed-point algorithm. Moreover, in order to obtain a better separation performance, we employ a mechanism to choose appropriate sample points from an initially selected sample vector. Finally, some computer simulations are presented to demonstrate the validity and effectiveness of the proposed method. | ||||||||||
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