Date
10.05.2005

Time
12:15

Nordlysobservatoriet

"Solving the Blind Separation Problem with FastICA", Erkki Oja, HUT

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. 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.

In the lecture, the basic ICA model and the FastICA algorithm are
first reviewed, followed by some recent results on the convergence of
the algorithm, another variant of BSS called Denoising Source
Separation, and applications on biomedical imagery and world climate
data.

Kontaktperson
Torbjørn Eltoft

Epost
torbjorn.eltoft@phys.uit.no

Tlf/mobil
95007345