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A least absolute bound approach to ICA: application to the MLSP 2006 competition

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Lee, John Aldo [UCL] Vrins, Frédéric [UCL] Verleysen, Michel [UCL]

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.

Document type Communication à un colloque (Conference Paper) – Présentation orale avec comité de sélection
Access type Accès restreint
Publication date 2006
Language Anglais
Conference "IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2006)", Maynooth, Ireland (du 06/09/2006 au 08/09/2006)
Host document "Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing"- 41-46 (ISBN : 1-4244-0657-9)
Publisher IEEE
Affiliation UCL - FSA/ELEC - Département d'électricité
Links
Bibliographic reference Lee, John Aldo ; Vrins, Frédéric ; Verleysen, Michel. A least absolute bound approach to ICA: application to the MLSP 2006 competition.IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2006) (Maynooth, Ireland, du 06/09/2006 au 08/09/2006). In: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, IEEE2006, p.41-46
Permanent URL http://hdl.handle.net/2078.1/67743