Auto-regressive independent process analysis without combinatorial efforts

Zoltán Szabó, Barnabás Póczos, András Lorincz

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We treat the problem of searching for hidden multi-dimensional independent auto-regressive processes (auto-regressive independent process analysis, AR-IPA). Independent subspace analysis (ISA) can be used to solve the AR-IPA task. The so-called separation theorem simplifies the ISA task considerably: the theorem enables one to reduce the task to one-dimensional blind source separation task followed by the grouping of the coordinates. However, the grouping of the coordinates still involves two types of combinatorial problems: (a) the number of the independent subspaces and their dimensions, and then (b) the permutation of the estimated coordinates are to be determined. Here, we generalize the separation theorem. We also show a non-combinatorial procedure, which-under certain conditions-can treat these two combinatorial problems. Numerical simulations have been conducted. We investigate problems that fulfill sufficient conditions of the theory and also others that do not. The success of the numerical simulations indicates that further generalizations of the separation theorem may be feasible.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalPattern Analysis and Applications
Volume13
Issue number1
DOIs
Publication statusPublished - Jan 1 2010

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Keywords

  • Auto-regressive processes
  • Independent component analysis
  • Independent process analysis

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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