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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 (Lecture Notes Comp. Sci. ; 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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Publication type Article: Edited volume or book chapter
Editors Adali, T.*