PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Complete blind subspace deconvolution
Zoltan Szabo
In: 8th International Conference on Independent Component Analysis and Signal Separation (ICA), 15-18 Mar 2009, Paraty, Brazil.

This is the latest version of this eprint.

Abstract

In this paper we address the blind subspace deconvolution (BSSD) problem; an extension of both the blind source deconvolution (BSD) and the independent subspace analysis (ISA) tasks. While previous works have been focused on the undercomplete case, here we extend the theory to complete systems. Particularly, we derive a separation technique for the complete BSSD problem: we solve the problem by reducing the estimation task to ISA via linear prediction. Numerical examples illustrate the efficiency of the proposed method.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Additional Information:http://dx.doi.org/10.1007/978-3-642-00599-2_18
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9528
Deposited By:Zoltan Szabo
Deposited On:29 May 2012

Available Versions of this Item

[Edit]