Complex-Weighted One-Unit ‘Rigid-Bodies’ Learning Rule for Independent Component Analysis

摘要

Over the recent years, noticeable theoretical efforts have been devoted to the understanding of the role of networks' parameter spaces in neural learning. One of the contributions in this field concerns the study of weight-flows on Stiefel manifold, which is the natural parameter-space's algebraic-structure in some unsupervised (information-theoretic) learning task. An algorithm belonging to the class of learning equations generating Stiefel-flows is based on the ‘rigid-body’ theory, introduced by the present Author in 1996. The aim of this Letter is to present an investigation on the capability of a complex-weighted neuron, trained by a ‘rigid-bodies’ learning theory, with application to blind source separation of complex-valued independent signals for telecommunication systems.