We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of ...
Constantinos Constantinopoulos, Michalis K. Titsia...
This paper proposes a novel method for blindly separating unobservable independent component (IC) signals based on the use of a genetic algorithm. It is intended for its applicati...
Blind separation of sources from nonlinear mixtures is a challenging and often ill-posed problem. We present three methods for solving this problem: an improved nonlinear factor a...
Antti Honkela, Harri Valpola, Alexander Ilin, Juha...
This correspondence presents new approaches for optimizing kurtosis-based separation criteria in the case of long mixture recordings. Our methods are based on a multivariate polyno...
In this paper, we present a new algorithm for solving the permutation ambiguity in convolutive blind source separation. A common approach for separation of convolutive mixtures is...