Abstract 
Independent Component Analysis (ICA) (see http://www.cis.hut.fi/projects/ica/) is a computational technique for revealing hidden factors that underlie sets of measurements or signals. The term blind source separation is often used to characterize this problem. ICA assumes a statistical model whereby the observed multivariate data, typically given as a large database of samples, are assumed to be linear or nonlinear mixtures of some unknown latent variables. The mixing coefficients are also unknown. The latent variables are nongaussian and mutually independent, and they are called the independent components of the observed data. By ICA, these independent components, also called sources or factors, can be found. Thus ICA can be seen as an extension to Principal Component Analysis and Factor Analysis. ICA is a much richer technique, however, capable of finding the sources when these classical methods fail completely. ICA separation can sometimes be enhanced by semiblind techniques such as temporal filtering in order to use prior knowledge.
In many cases, the measurements are given as a set of parallel signals or images. Two examples covered in the talk are 3dimensional brain images obtained by functional MRI, and longterm climate measurements over the globe, both having dimensionalities in the tens of thousands. Recent results are shown on brain activations to stimuli and on climate patterns.
