Hierarchical extraction of independent subspaces of unknown dimensions

Peter Gruber, Harold W. Gutch, Fabian J. Theis

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

Independent Subspace Analysis (ISA) is an extension of Independent Component Analysis (ICA) that aims to linearly transform a random vector such as to render groups of its components mutually independent. A recently proposed fixed-point algorithm is able to locally perform ISA if the sizes of the subspaces are known, however global convergence is a serious problem as the proposed cost function has additional local minima. We introduce an extension to this algorithm, based on the idea that the algorithm converges to a solution, in which subspaces that are members of the global minimum occur with a higher frequency. We show that this overcomes the algorithm's limitations. Moreover, this idea allows a blind approach, where no a priori knowledge of subspace sizes is required.

Original languageEnglish
Pages (from-to)259-266
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5441
DOIs
StatePublished - 2009
Externally publishedYes
Event8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009 - Paraty, Brazil
Duration: 15 Mar 200918 Mar 2009

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