Research activities

Prof. Juha Karhunen belongs to two research groups in the Department of Information and Computer Science:

- Bayesian algorithms for latent variable models

- Independent component analysis

From the web pages of these research groups, especially on their subpages "Research", you can find descriptions on their research topics.

The "Bayesian algorithms for latent variable models" research group has applied variational Bayesian learning (earlier called also Bayesian ensemble learning) methods to unsupervised or blind learning problems for continuous-valued data. The data is modeled using either neural networks or other graphical models. Recently the group has considered also probabilistic modeling in deep learning. Detailed information can be found in the research reports of our ICA group, covering the years 2008-2009, 2006-2007, 2004-2005, 2002-2003, and 2000-2001.

The "Independent component analysis" research group studies not only independent component analysis (ICA), but also various blind source separation (BSS) methods, as well as non-negative matrix factorizations. Detailed information can be found in the research reports of our ICA group, covering the years 2008-2009, 2006-2007, 2004-2005, 2002-2003, as well as theoretical ICA research in 2000-2001, and applications of ICA in 2000-2001.

I have studied also robust principal component analysis (PCA) and missing values in PCA, see my recent publications.

Information about my older research efforts on nonlinear PCA, ICA and BSS, and their extensions, as well as on subspace methods in CDMA can be found in my publications as well as in the triennial report (covering the years 1997-1999) and quinquennial report (covering the years 1994-1998) of our laboratory, available here.