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 …

We derive an asymptotic Newton algorithm for Quasi Maximum Likelihood estimation of the
ICA mixture model, using the ordinary gradient and Hessian. The probabilistic mixture …

We give a general overview of the use and possible misuse of blind source separation
(BSS) and independent component analysis (ICA) in the context of neuroinformatics data …

In this paper, we review recent advances in blind source separation (BSS) and independent
component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS …

A new algorithmic framework called denoising source separation (DSS) is introduced. The
main benefit of this framework is that it allows for the easy development of new source …

We consider criteria for variational representations of non-Gaussian latent variables, and
derive variational EM algorithms in general form. We establish a general equivalence …

In this chapter, ensemble learning is applied to the problem of blind source separation and
deconvolution of images. It is assumed that the observed images were constructed by …

A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic
processes from noisy data. The data are assumed to be generated by an unknown nonlinear …

The present paper is written as a word of caution, with users of independent component
analysis (ICA) in mind, to overlearning phenomena that are often observed. We consider two …

The fundamental area of research in this thesis is Independent Component Analysis (ICA).
ICA is a tool for discovering structure and patterns in data by factoring a multidimensional …