Algebraic Independent Component Analysis (AICA) is a new ICA algorithm that exploits algebraic operations and vectordistance measures to estimate the unknown mixing matrix in a scaled algebraic domain. AICA possesses stability and convergence properties similar to earlier proposed geometic ICA (geo-ICA) algorithms, however, the choice of the proposed algebraic measures in AICA has several advan...

A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Well-known linear transformation methods include, for example, principal componen...

The analysis of electroencephalographic (EEG) recording is important both for brain research and for medical diagnosis and treatment. Independent Component Analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from the EEG recordings. Results show that ICA is a useful technique for the evaluation of different variables in the brain activity.

We propose a mixture model for blind source separation and deconvolution with adaptive source densities. Data is modelled as a multivariate locally linear random process. We derive an expression for the asymptotic likelihood of a linear process segment, which allows us to formulate and optimize a mixture model via the EM algorithm. The mixture model is able to represent nonstationary (locally, ...

We propose a parametric version of Independent Component Analysis (ICA) via Copulas families of multivariate distributions that join univariate margins to multivariate distributions. Our procedure exploits the role for copula models in information theory and in measures of association, specifically: the use of copulae densities as parametric mutual information, and as measures of association on...