Neurally plausible, non-combinatorial iterative independent process analysis
András Lorincz and Zoltan Szabo
Neurocomputing - Letters Volume 70, Number 7-9, pp. 1569-1573, 2006.

There is a more recent version of this eprint available. Click here to view it.

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.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.

Available Versions of this Item

• Neurally plausible, non-combinatorial iterative independent process analysis (deposited 01 December 2011) [Currently Displayed]
  • Neurally plausible, non-combinatorial iterative independent process analysis (deposited 29 May 2012)

[Edit]