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IDA
2009
Springer

Hierarchical Extraction of Independent Subspaces of Unknown Dimensions

8 years 8 months ago
Hierarchical Extraction of Independent Subspaces of Unknown Dimensions
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. Assuming an independent random vector S that is mixed by an unknown mixing matrix A, Independent Component Analysis (ICA) denotes the task of recovering S, given only the mixed signals, X = AS. It is kn...
Peter Gruber, Harold W. Gutch, Fabian J. Theis
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where IDA
Authors Peter Gruber, Harold W. Gutch, Fabian J. Theis
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