A characteristic function based contrast function for blind extraction of statistically independent signals

Muhammad Tufail, Masahide Abe, Masayuki Kawamata

研究成果: Article査読

抄録

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.

本文言語English
ページ(範囲)2149-2157
ページ数9
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E89-A
8
DOI
出版ステータスPublished - 2006 8

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学

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