Independent component analysis is a probabilistic method for learning a linear transform of
a random vector. The goal is to find components that are maximally independent and non-…

Independent component analysis (ICA) is a statistical method for transforming an observed
multidimensional random vector into components that are statistically as independent from …

A fundamental problem in neural network research, as well as in many other disciplines, is
finding a suitable representation of multivariate data, ie random vectors. For reasons of …

In this paper we describe and summarize the main achievements of the European Aerosol
Cloud Climate and Air Quality Interactions project (EUCAARI). EUCAARI started on 1 January …

This review discusses the role of fungi and fungal products in indoor environments, especially
as agents of human exposure. Fungi are present everywhere, and knowledge for indoor …

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 …

One often wants to estimate statistical models where the probability density function is
known only up to a multiplicative normalization constant. Typically, one then has to resort to …

Summarising a high dimensional data set with a low dimensional embedding is a standard
approach for exploring its structure. In this paper we provide an overview of some existing …

Recently, independent component analysis (ICA) has been widely used in the analysis of
brain imaging data. An important problem with most ICA algorithms is, however, that they are …

We consider the task of estimating, from observed data, a probabilistic model that is parameterized
by a finite number of parameters. In particular, we are considering the situation where …