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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)
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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.
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Publikationstyp Artikel: Sammelbandbeitrag/Buchkapitel
Herausgeber Adali, T.*
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Bandtitel Independent Component Analysis and Signal Separation
Quellenangaben Band: 5441, Heft: , Seiten: 259-266 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]