Real and Complex Independent Subspace Analysis by Generalized Variance
Statistics Theory
2012-01-04 v1 Statistics Theory
Abstract
Here, we address the problem of Independent Subspace Analysis (ISA). We develop a technique that (i) builds upon joint decorrelation for a set of functions, (ii) can be related to kernel based techniques, (iii) can be interpreted as a self-adjusting, self-grouping neural network solution, (iv) can be used both for real and for complex problems, and (v) can be a first step towards large scale problems. Our numerical examples extend to a few 100 dimensional ISA tasks.
Keywords
Cite
@article{arxiv.math/0610438,
title = {Real and Complex Independent Subspace Analysis by Generalized Variance},
author = {Zoltan Szabo and Andras Lorincz},
journal= {arXiv preprint arXiv:math/0610438},
year = {2012}
}
Comments
Presented at ICARN 2006, 4 pages