Independent component analysis (ICA) is increasing in popularity in the field of biomedical
signal processing. It is generally used when it is required to separate measured multi …

Face recognition is a rapidly growing research area due to increasing demands for security
in commercial and law enforcement applications. This paper provides an up-to-date review …

We marry ideas from deep neural networks and approximate Bayesian inference to derive a
generalised class of deep, directed generative models, endowed with a new algorithm for …

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 …

Not so long ago, multivariate analysis consisted solely of linear methods illustrated on small
to medium-sized data sets. Moreover, statistical computing meant primarily batch processing …

A number of current face recognition algorithms use face representations found by
unsupervised statistical methods. Typically these methods find a set of basis images and …

Advances in data collection and storage capabilities during the past decades have led to an
information overload in most sciences. Researchers working in domains as diverse as …

The analysis of electroencephalographic and magnetoencephalographic recordings is
important both for basic brain research and for medical diagnosis and treatment …

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 …

Lithology identification based on conventional well-logging data is of great importance for
geologic features characterization and reservoir quality evaluation in the exploration and …