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

Jianting Cao, Noboru Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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
Title of host publicationNeural Networks for Signal Processing - Proceedings of the IEEE Workshop
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages283-292
Number of pages10
Publication statusPublished - 1999
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

    Fingerprint

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Cite this

Cao, J., & Murata, N. (1999). Stable and robust ICA algorithm based on t-distribution and generalized Gaussian distribution models. In Neural Networks for Signal Processing - Proceedings of the IEEE Workshop (pp. 283-292). IEEE.