In this chapter, we discuss a statistical generative model called independent component
analysis. It is basically a proper probabilistic formulation of the ideas underpinning sparse …

With solid theoretical foundations and numerous potential applications, Blind Signal
Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume …

Blind signal separation (BSS) and independent component analysis (ICA) are emerging
techniques of array processing and data analysis that aim to recover unobserved signals or" …

This article considers high-order measures of independence for the independent component
analysis problem and discusses the class of Jacobi algorithms for their optimization. Several …

Learning algorithms and underlying basic mathematical ideas are presented for the problem
of adaptive blind signal processing, especially instantaneous blind separation and …

In the field of blind source separation, joint-diagonalization-based approaches constitute an
important framework, leading to useful algorithms such as the popular joint approximate …

We revise the problem of extracting one independent component from an instantaneous
linear mixture of signals. The mixing matrix is parameterized by two vectors: one column of …

Blind source separation (BSS) is a general signal processing method, which consists of
recovering, from a finite set of observations recorded by sensors, the contributions of …

We present new results on the nonlinear principal component analysis (PCA) criterion in
blind source separation (BSS). We derive the criterion in a form that allows easy …

Independent component analysis or blind source separation extracts independent signals
from their linear mixtures without assuming prior knowledge of their mixing coefficients. It is …