PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Separation theorem for K-independent subspace analysis with sufficient conditions
Zoltan Szabo, Barnabas Poczos and András Lorincz
(2006) Technical Report, arXiv.

Abstract

Here, a Separation Theorem about K-Independent Subspace Analysis (K real or complex), a generalization of K-Independent Component Analysis (KICA) is proven. According to the theorem, KISA estimation can be executed in two steps under certain conditions. In the first step, one-dimensional KICA estimation is executed. In the second step, optimal permutation of the KICA elements is searched for. We present sufficient conditions for the KISA Separation Theorem. Namely, we shall show that (i) spherically symmetric sources (both for real and complex cases), as well as (ii) real 2-dimensional sources invariant to 90 degree rotation, among others, satisfy the conditions of the theorem.

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EPrint Type:Other
Additional Information:http://arxiv.org/abs/math.ST/0608100
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8367
Deposited By:Zoltan Szabo
Deposited On:01 December 2011