Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Natural gradient works efficiently in learning
Neural Computation
Independent component analysis: theory and applications
Independent component analysis: theory and applications
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
On the momentum term in gradient descent learning algorithms
Neural Networks
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Neural Network Based Processing for Smart Sensors Arrays
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A Theory for Learning by Weight Flow on Stiefel-Grassman Manifold
Neural Computation
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
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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.