A fixed-point algorithm for blind source separation with nonlinear autocorrelation

Authors:
Zhenwei Shi;Zhiguo Jiang;Fugen Zhou
Affiliations:
Image Processing Center, School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083, PR China;Image Processing Center, School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083, PR China;Image Processing Center, School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083, PR China
Venue:
Journal of Computational and Applied Mathematics
Year:
2009

Citing 27
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
A neural net for blind separation of nonstationary signals

Neural Networks
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Independent component analysis for identification of artifacts in magnetoencephalographic recordings

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources

Neural Computation
Optimization by Vector Space Methods

Optimization by Vector Space Methods
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Analysis of sparse representation and blind source separation

Neural Computation
Blind separation of positive sources by globally convergent gradient search

Neural Computation
Extraction of Specific Signals with Temporal Structure

Neural Computation
Blind Source Separation Using Temporal Predictability

Neural Computation
Blind Source Separation by Sparse Decomposition in a Signal Dictionary

Neural Computation
Complexity Pursuit: Separating Interesting Components from Time Series

Neural Computation
Learning Overcomplete Representations

Neural Computation
Blind source separation of more sources than mixtures using sparse mixture models

Pattern Recognition Letters
Letters: Semi-blind source extraction for fetal electrocardiogram extraction by combining non-Gaussianity and time-correlation

Neurocomputing
Letters: Nonlinear innovation to blind source separation

Neurocomputing
A new constrained fixed-point algorithm for ordering independent components

Journal of Computational and Applied Mathematics
Letters: A fast fixed-point algorithm for complexity pursuit

Neurocomputing
Letters: Gaussian moments for noisy complexity pursuit

Neurocomputing
A blind source separation technique using second-order statistics

IEEE Transactions on Signal Processing
Equivariant adaptive source separation

IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources

IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks
Blind source separation by nonstationarity of variance: a cumulant-based approach

IEEE Transactions on Neural Networks
A "nonnegative PCA" algorithm for independent component analysis

IEEE Transactions on Neural Networks

Blind source separation with nonlinear autocorrelation and non-Gaussianity

Journal of Computational and Applied Mathematics
Fast nonlinear autocorrelation algorithm for source separation

Pattern Recognition
Hybrid linear and nonlinear complexity pursuit for blind source separation

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	7.29

Visualization

Abstract

This paper addresses blind source separation (BSS) problem when source signals have the temporal structure with nonlinear autocorrelation. Using the temporal characteristics of sources, we develop an objective function based on the nonlinear autocorrelation of sources. Maximizing the objective function, we propose a fixed-point source separation algorithm. Furthermore, we give some mathematical properties of the algorithm. Computer simulations for sources with square temporal autocorrelation and the real-world applications in the analysis of the magnetoencephalographic recordings (MEG) illustrate the efficiency of the proposed approach. Thus, the presented BSS algorithm, which is based on the nonlinear measure of temporal autocorrelation, provides a novel statistical property to perform BSS.