Neurally plausible, non-combinatorial iterative independent process analysis

András Lorincz, Zoltán Szabó

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

It has been shown recently that the identification of mixed hidden independent auto-regressive processes (independent process analysis, IPA), under certain conditions, can be free from combinatorial explosion. The key is that IPA can be reduced (i) to independent subspace analysis and then, via a novel decomposition technique called Separation Theorem, (ii) to independent component analysis. Here, we introduce an iterative scheme and its neural network representation that takes advantage of the reduction method and can accomplish the IPA task. Computer simulation illustrates the working of the algorithm.

Original languageEnglish
Pages (from-to)1569-1573
Number of pages5
JournalNeurocomputing
Volume70
Issue number7-9
DOIs
Publication statusPublished - Mar 1 2007

Keywords

  • Independent process analysis
  • Neural network implementation

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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