New equations and iterative algorithm for blind separation of sources

Authors:
Rubén Martín-Clemente;José I. Acha
Affiliations:
Área de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Escuela Superior de Ingenieros, Avda. de los Descubrimientos s/n., 41092 Sevilla, Spain;Área de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Escuela Superior de Ingenieros, Avda. de los Descubrimientos s/n., 41092 Sevilla, Spain
Venue:
Signal Processing
Year:
2002

Citing 11
Cited 2

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
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
A fast fixed-point algorithm for independent component analysis

Neural Computation
A neural learning algorithm for blind separation of sources based on geometric properties

Signal Processing - Special issue on neural networks
An algebraic principle for blind separation of white non-Gaussian sources

Signal Processing
Discrete Random Signals and Statistical Signal Processing

Discrete Random Signals and Statistical Signal Processing
General approach to blind source separation

IEEE Transactions on Signal Processing
A direct solution for blind separation of sources

IEEE Transactions on Signal Processing

Eigendecomposition of self-tuned cumulant-matrices for blind source separation

Signal Processing
A computer-aided diagnosis system for breast cancer using independent component analysis and fuzzy classifier

Modelling and Simulation in Engineering - Modelling and simulation: computational intelligence in medicine

Quantified Score

Hi-index	0.08

Visualization

Abstract

This paper addresses the blind separation of non-gaussian sources for instantaneous mixtures by using higher-order statistics. It is proven that the estimation of the separating matrix can be achieved by solving linear equations repeatedly. The convergence of the algorithm is proven analytically. A study of the complexity of the algorithm is also presented.