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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)
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
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 5441,
Seiten: 259-266
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
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