PuSH - Publication Server of Helmholtz Zentrum München

Gruber, P.* ; Gutch, H.W.* ; Theis, F.J.

Hierarchical extraction of independent subspaces of unknown dimensions.

In: Independent Component Analysis and Signal Separation. Berlin [u.a.]: Springer, 2009. 259-266 (Lect. Notes Comput. Sc. ; 5441)
DOI Order publishers version
Open Access Green as soon as Postprint is submitted to ZB.
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.
Altmetric
Additional Metrics?
Tags
Icb_ML
Edit extra informations Login
Publication type Article: Edited volume or book chapter
Editors Adali, T.*
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Book Volume Title Independent Component Analysis and Signal Separation
Quellenangaben Volume: 5441, Issue: , Pages: 259-266 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]