Independent subspace analysis using geodesic spanning trees

Barnabás Póczos, András Lórincz

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

17 Citations (Scopus)

Abstract

A novel algorithm for performing Independent Subspace Analysis, the estimation of hidden independent subspaces is introduced. This task is a generalization of Independent Component Analysis. The algorithm works by estimating the multi-dimensional differential entropy. The estimation utilizes minimal geodesic spanning trees matched to the sample points. Numerical studies include (i) illustrative examples, (ii) a generalization of the cocktail-party problem to songs played by bands, and (iii) an example on mixed independent subspaces, where subspaces have dependent sources, which are pairwise independent.

Original languageEnglish
Title of host publicationICML 2005 - Proceedings of the 22nd International Conference on Machine Learning
PublisherAssociation for Computing Machinery (ACM)
Pages673-680
Number of pages8
ISBN (Print)1595931805, 9781595931801
DOIs
Publication statusPublished - Jan 1 2005
EventICML 2005: 22nd International Conference on Machine Learning - Bonn, Germany
Duration: Aug 7 2005Aug 11 2005

Publication series

NameICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Other

OtherICML 2005: 22nd International Conference on Machine Learning
CountryGermany
CityBonn
Period8/7/058/11/05

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Póczos, B., & Lórincz, A. (2005). Independent subspace analysis using geodesic spanning trees. In ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning (pp. 673-680). (ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning). Association for Computing Machinery (ACM). https://doi.org/10.1145/1102351.1102436