Low Complexity Functions for Stationary Independent Component Mixtures

K. Chinnasarn, C. Lursinsap, V. Palade

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

Abstract

Obtaining a low complexity activation function and an online sub-block learning for non-gaussian mixtures are presented in this paper. The paper deals with independent component analysis with mutual information as a cost function. First, we propose a low complexity activation function for non-gaussian mixtures, and then an online sub-block learning for stationary mixture is introduced. The size of the sub-blocks is larger than the maximal frequency Fmax of the principal component of the original signals. Experimental results proved that the proposed activation function and the online sub-block learning method are more efficient in terms of computational complexity as well as in terms of learning ability.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 7th International Conference, KES 2003, Proceedings
EditorsVasile Palade, Robert J. Howlett, Lakhmi Jain
Place of PublicationBerlin
PublisherSpringer Verlag
Pages653-669
Number of pages17
Volume2773
ISBN (Electronic)978-3-540-45224-9
ISBN (Print)978-3-540-40803-1
DOIs
Publication statusPublished - 2003
Externally publishedYes
Event7th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2003 - Oxford, United Kingdom
Duration: 3 Sep 20035 Sep 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2773
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference7th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2003
CountryUnited Kingdom
CityOxford
Period3/09/035/09/03

Keywords

  • Blind signal separation
  • Independent component analysis
  • Mutual information
  • Unsupervised neural networks

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Chinnasarn, K., Lursinsap, C., & Palade, V. (2003). Low Complexity Functions for Stationary Independent Component Mixtures. In V. Palade, R. J. Howlett, & L. Jain (Eds.), Knowledge-Based Intelligent Information and Engineering Systems - 7th International Conference, KES 2003, Proceedings (Vol. 2773, pp. 653-669). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2773). Berlin: Springer Verlag. https://doi.org/10.1007/978-3-540-45224-9_90