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Baysesian methods and software for blind source separation of nonlinear, non-independent, and dynamic mixtures

The Bayes group in the Laboratory of Computer and Information Science and Neural Networks Research Centre in Helsinki University of Technology, Espoo, Finland has developed Bayesian methods, which are suitable for unsupervised learning. We have studied Bayesian ensemble learning, which is one type of variational Bayesian learning, and applied it to various latent variable model structures.

In particular, in the BLISS project we have introduced and studied variational Bayesian methods for blind source separation of nonlinear, non-independent, and dynamic mixtures. A major research line is development of modular building blocks which can be used for constructing nonlinear and non-Gaussian model and which allow automated derivation of learning rules.

We have successfully applied our methods to various real-world data sets. In particular, we have released several free software packages, which are available on the home page of the Bayes group There are also selected most important publications. The main results achieved have been reported in the deliverables of the BLISS project.

Bayesian data analysis was listed among the 10 most promising areas of new technology in a respected technological review in 2004. Thus the results achieved have a wide application potential to various data analysis and modeling problems. We have already applied our methods to analysis of video image sequences, astronomical images, color description, speech data, biomedical MEG data, and control problems, as well as to reconstruction of missing values in various data sets. For more details, see the publications, which can be found on our home page.

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Aalto University School of Science and Technology,
P.O. Box 5400
00076 ESPOO
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