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

Jianting Cao, Noboru Murata

Research output: Contribution to conferencePaper

12 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages283-292
Number of pages10
Publication statusPublished - 1999 Dec 1
Externally publishedYes
EventProceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99) - Madison, WI, USA
Duration: 1999 Aug 231999 Aug 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

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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    Cao, J., & Murata, N. (1999). Stable and robust ICA algorithm based on t-distribution and generalized Gaussian distribution models. 283-292. Paper presented at Proceedings of the 1999 9th IEEE Workshop on Neural Networks for Signal Processing (NNSP'99), Madison, WI, USA, .