From Blind Signal Extraction to Blind Instantaneous Signal Separation: Criteria, Algorithms, and Stability

S.A. Cruces-Alvarez, A. Cichocki, S. Amari
2004 IEEE Transactions on Neural Networks  
This paper reports a study on the problem of the blind simultaneous extraction of specific groups of independent components from a linear mixture. This paper first presents a general overview and unification of several information theoretic criteria for the extraction of a single independent component. Then, our contribution fills the theoretical gap that exists between extraction and separation by presenting tools that extend these criteria to allow the simultaneous blind extraction of subsets
more » ... with an arbitrary number of independent components. In addition, we analyze a family of learning algorithms based on Stiefel manifolds and the natural gradient ascent, present the nonlinear optimal activations (score) functions, and provide new or extended local stability conditions. Finally, we illustrate the performance and features of the proposed approach by computer-simulation experiments. Index Terms-Blind-signal extraction, blind signal separation, independent component analysis, negentropy and minimum entropy, projection pursuit. Japan, on several occasions. He is currently an Associate Professor at the University of Seville, Spain, where he has been a member of Signal Theory and Communications Group since 1995. He teaches undergraduate and graduate courses on digital signal processing of speech signals and mathematical methods for communication. His current research interests include statistical signal processing, information theoretic and neural network approaches, blind equalization, and filter stabilization techniques.
doi:10.1109/tnn.2004.828764 pmid:15461079 fatcat:3gkrmacfevbnhha7w3q6v3eyxq