Stable and robust ICA algorithm based on t-distribution and generalized Gaussian distribution models

Jianting Cao*, Noboru Murata

*この研究の対応する著者

研究成果: Paper査読

14 被引用数 (Scopus)

抄録

In this paper, we propose a novel Independent Component Analysis (ICA) algorithm which enables to separate mixtures of sub-Gaussian, super-Gaussian and Gaussian primary source signals. Alternative activation functions in the algorithm are derived by using parameterized t-distribution and generalized Gaussian distribution density models. The functions are self-adaptive based on estimating the high-order moments of extracted signals. Moreover, a stability condition of the proposed algorithm for separating the true solution is given. Simulation experiment results are presented to illustrate the effectiveness and performance of the proposed algorithm.

本文言語English
ページ283-292
ページ数10
出版ステータスPublished - 1999 12 1
外部発表はい
イベントProceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) - Madison, WI, USA
継続期間: 1999 8 231999 8 25

Other

OtherProceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99)
CityMadison, WI, USA
Period99/8/2399/8/25

ASJC Scopus subject areas

  • 信号処理
  • ソフトウェア
  • 電子工学および電気工学

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