A minimum-range approach to blind extraction of bounded sources.
In spite of the numerous approaches that have been derived for solving the independent component analysis (ICA) problem, it is still interesting to develop new methods when, among other reasons, specific a priori knowledge may help to further improve the separation performances. In this paper, the minimum-range approach to blind extraction of bounded source is investigated. The relationship with other existing well-known criteria is established. It is proved that the minimum-range approach is a contrast, and that the criterion is discriminant in the sense that it is free of spurious maxima. The practical issues are also discussed, and a range measure estimation is proposed based on the order statistics. An algorithm for contrast maximization over the group of special orthogonal matrices is proposed. Simulation results illustrate the performances of the algorithm when using the proposed range estimation criterion.
Vrins
Frédéric
F
Microelectronics Lab (DICE), Université catholique de Louvain, Louvain-la-Neuve 1348, Belgium. vrins@dice.ucl.ac.be
Lee
John A
JA
Verleysen
Michel
M
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis