From blind signal extraction to blind instantaneous signal separation: criteria, algorithms, and stability

Authors:
S. A. Cruces-Alvarez;A. Cichocki;S. Amari
Affiliations:
Univ. de Sevilla, Spain;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2004

Citing 0
Cited 21

On the entropy minimization of a linear mixture of variables for source separation

Signal Processing - Special issue: Information theoretic signal processing
Blind source extraction: Standard approaches and extensions to noisy and post-nonlinear mixing

Neurocomputing
Nonlinear Innovation to Noisy Blind Source Separation Based on Gaussian Moments

ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Semi-blind source extraction algorithm for fetal electrocardiogram based on generalized autocorrelations and reference signals

Journal of Computational and Applied Mathematics
Blind Source Extraction for Noisy Mixtures by Combining Gaussian Moments and Generalized Autocorrelations

Neural Processing Letters
Blind source extraction based on generalized autocorrelations and complexity pursuit

Neurocomputing
Information theoretical algorithm based on statistical models for blind identification of nonstationary dynamical systems

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Letters: Extraction of temporally correlated sources with its application to non-invasive fetal electrocardiogram extraction

Neurocomputing
Letters: Extraction of a source signal whose kurtosis value lies in a specific range

Neurocomputing
Robust independent component analysis by iterative maximization of the kurtosis contrast with algebraic optimal step size

IEEE Transactions on Neural Networks
The complex version of the minimum support criterion

ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Source extraction by maximizing the variance in the conditional distribution tails

IEEE Transactions on Signal Processing
A contrast function for independent component analysis without permutation ambiguity

IEEE Transactions on Neural Networks
QML-based joint diagonalization of positive-definite hermitian matrices

IEEE Transactions on Signal Processing
Temporally local maximum signal fraction analysis for artifact removal from biomedical signals

IEEE Transactions on Signal Processing
Bounded component analysis of linear mixtures: a criterion of minimum convex perimeter

IEEE Transactions on Signal Processing
Noisy component extraction (NoiCE)

IEEE Transactions on Circuits and Systems Part I: Regular Papers
A two-stage based approach for extracting periodic signals

ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Novel nonlinear signals separation of optimized entropy based on adaptive natural gradient learning

ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Regulatory component analysis: A semi-blind extraction approach to infer gene regulatory networks with imperfect biological knowledge

Signal Processing
The CAM software for nonnegative blind source separation in R-Java

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.02

Visualization

Abstract

This paper reports a study on the problem of the blind simultaneous extraction of specific groups of independent components from a linear mixture. This paper first presents a general overview and unification of several information theoretic criteria for the extraction of a single independent component. Then, our contribution fills the theoretical gap that exists between extraction and separation by presenting tools that extend these criteria to allow the simultaneous blind extraction of subsets with an arbitrary number of independent components. In addition, we analyze a family of learning algorithms based on Stiefel manifolds and the natural gradient ascent, present the nonlinear optimal activations (score) functions, and provide new or extended local stability conditions. Finally, we illustrate the performance and features of the proposed approach by computer-simulation experiments.