A Conjugate Gradient Method and Simulated Annealing for Blind Separation of Sources

Authors:
Rubén Martín-Clemente;Carlos García Puntonet;José I. Acha
Affiliations:
-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Year:
2001

Citing 9
Cited 1

Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing

Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture

Signal Processing
Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Separation of sources: a geometry-based procedure for reconstruction of n-valued signals

Signal Processing
A fast fixed-point algorithm for independent component analysis

Neural Computation
Alternating projections onto convex sets

Deconvolution of images and spectra (2nd ed.)
A neural learning algorithm for blind separation of sources based on geometric properties

Signal Processing - Special issue on neural networks
The Geometry of Algorithms with Orthogonality Constraints

SIAM Journal on Matrix Analysis and Applications

A novel conjugate gradient based block adaptive ICA algorithm for dynamic environments

ICECS'05 Proceedings of the 4th WSEAS international conference on Electronics, control and signal processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new procedure for Blind Separtion of non-Gaussian Sources. It is proven that the estimation of the separating system can be based on the cancellation of some second partial derivatives of the output cross-cumulants. The resulting method is based on a conjutate gradient algorithm on the Stiefel manifold. Simulated annealing is used to obtain a good initial value and improve the rate of convergence.