Title :
A Least Absolute Bound Approach to ICA - Application to the MLSP 2006 Competition
Author :
Lee, John A. ; Vrins, Frédéric ; Verleysen, Michel
Author_Institution :
Machine Learning Group, Univ. catholique de Louvain, Louvain-la-Neuve
Abstract :
This paper describes a least absolute bound approach as a way to solve the ICA problems proposed in the 2006 MSLP competition. The least absolute bound is an ICA contrast closely related to the support width measure, which has been already studied for the blind extraction of bounded sources. By comparison, the least absolute bound applies to a broader class of sources, including those that are bounded on a single side only. This precisely corresponds to the sources involved in the competition. Practically, the minimization of the least absolute bound relies on a specific deflation algorithm with a loose orthogonality constraint. This allows solving large-scale problems without accumulating errors.
Keywords :
blind source separation; independent component analysis; minimisation; MLSP 2006 competition; bounded sources blind extraction; deflation algorithm; independent component analysis; least absolute bound approach; least absolute bound minimization; Independent component analysis; Iterative algorithms; Laboratories; Large-scale systems; Machine learning; Microelectronics; Minimization methods; Sparse matrices; White noise; Yield estimation;
Conference_Titel :
Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on
Conference_Location :
Arlington, VA
Print_ISBN :
1-4244-0656-0
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2006.275519
