Cross-entropy optimization for independent process analysis

Zoltán Szabó, Barnabas Póczos, András Lõrincz

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

12 Citations (Scopus)

Abstract

We treat the problem of searching for hidden multi-dimensional independent auto-regressive processes. First, we transform the problem to Independent Subspace Analysis (ISA). Our main contribution concerns ISA. We show that under certain conditions, ISA is equivalent to a combinatorial optimization problem. For the solution of this optimization we apply the cross-entropy method. Numerical simulations indicate that the cross-entropy method can provide considerable improvements over other state-of-the-art methods.

Original languageEnglish
Title of host publicationIndependent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings
PublisherSpringer Verlag
Pages909-916
Number of pages8
ISBN (Print)3540326308, 9783540326304
DOIs
Publication statusPublished - 2006
Event6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 - Charleston, SC, United States
Duration: Mar 5 2006Mar 8 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3889 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006
CountryUnited States
CityCharleston, SC
Period3/5/063/8/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Szabó, Z., Póczos, B., & Lõrincz, A. (2006). Cross-entropy optimization for independent process analysis. In Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings (pp. 909-916). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3889 LNCS). Springer Verlag. https://doi.org/10.1007/11679363_113