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

Authors:
Rubén Martín-Clemente;José I. Acha;Carlos G. Puntonet
Affiliations:
Área de Teoría de la Señal y Comunicaciones, Escuela Superior de Ingenieros Universidad de Sevilla, Avda. de los Descubrimientos s/n., 41092 Sevilla, Spain;Área de Teoría de la Señal y Comunicaciones, Escuela Superior de Ingenieros Universidad de Sevilla, Avda. de los Descubrimientos s/n., 41092 Sevilla, Spain;Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, C/ Periodista Daniel Saucedo s/n, 18071 Granada, Spain
Venue:
Signal Processing
Year:
2004

Citing 13
Cited 3

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
Adaptive blind separation of independent sources: a deflation approach

Signal Processing
Matrix computations (3rd ed.)

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

Neural Computation
High-order contrasts for independent component analysis

Neural Computation
Array Signal Processing: Concepts and Techniques

Array Signal Processing: Concepts and Techniques
New equations and iterative algorithm for blind separation of sources

Signal Processing
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A blind source separation technique using second-order statistics

IEEE Transactions on Signal Processing
General approach to blind source separation

IEEE Transactions on Signal Processing
Multichannel blind separation of sources algorithm based oncross-cumulant and the Levenberg-Marquardt method

IEEE Transactions on Signal Processing
Independent component analysis and (simultaneous) third-ordertensor diagonalization

IEEE Transactions on Signal Processing

Effective blind separation of skewed sources

Signal Processing - Fractional calculus applications in signals and systems
Maximization of statistical moments for blind separation of sources revisited

Neurocomputing
Non-cancellation multistage kurtosis maximization with prewhitening for blind source separation

EURASIP Journal on Advances in Signal Processing

Quantified Score

Hi-index	0.08

Visualization

Abstract

Existing algorithms for blind source separation are often based on the eigendecomposition of fourth-order cumulant matrices. However, when the cumulant matrices have close eigenvalues, their eigenvectors are very sensitive to errors in the estimation of the matrices.In this paper, we show how to produce a cumulant matrix that has a well-separated extremal eigenvalue. The corresponding eigenvector is thus well conditioned and can be used to develop robust algorithms for blind source extraction. Some numerical experiments are provided to illustrate the effectiveness of the proposed approach.