A Constrained EM Algorithm for Independent Component Analysis

A Constrained EM Algorithm for Independent Component Analysis

We introduce a novel way of performing independent component analysis using a constrained version of the expectation-maximization (EM) algorithm. The source distributions are modeled as D one-dimensional mixtures of gaussians. The observed data are modeled as linear mixtures of the sources with additive, isotropic noise. This generative model is fit to the data using constrained EM. The simpler...

An EM algorithm for convolutive independent component analysis

In this paper, we address the problem of blind separation of convolutive mixtures of spatially and temporally independent sources modeled with mixtures of Gaussians. We present an EM algorithm to compute Maximum Likelihood estimates of both the separating filters and the source density parameters, whereas in the state-of-the-art separating filters are usually estimated with gradient descent tec...

Constrained Independent Component Analysis

The paper presents a novel technique of constrained independent component analysis (CICA) to introduce constraints into the classical ICA and solve the constrained optimization problem by using Lagrange multiplier methods. This paper shows that CICA can be used to order the resulted independent components in a specific manner and normalize the demixing matrix in the signal separation procedure....

Independent component analysis with the EM and FFT algorithm.

A Component-wise EM Algorithm for Mixtures

In some situations, EM algorithm shows slow convergence problems. One possible reason is that standard procedures update the parameters simultaneously. In this paper we focus on nite mixture estimation. In this framework, we propose a component-wise EM, which updates the parameters sequentially. We give an interpretation of this procedure as a proximal point algorithm and use it to prove the co...