Separation Theorem for K-Independent Subspace Analysis with Sufficient Conditions
Statistics Theory
2007-06-13 v3 Statistics Theory
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, 1-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.
Cite
@article{arxiv.math/0608100,
title = {Separation Theorem for K-Independent Subspace Analysis with Sufficient Conditions},
author = {Zoltan Szabo and Barnabas Poczos and Andras Lorincz},
journal= {arXiv preprint arXiv:math/0608100},
year = {2007}
}
Comments
Reference [13]: corrected