The performance of ICA algorithms significantly depends on the choice of the contrast function and the optimisation algorithm used in obtaining the demixing matrix. In this paper we focus on the standard linear nonparametric ICA problem from an optimisation point of view. It is well known that after a pre-whitening process, the problem can be solved via an optimisation approach on a suitable manifold. We propose an approximate Newton's method on the unit sphere to solve the one-unit linear nonparametric ICA problem. The local convergence properties are discussed. The performance of the proposed algorithms is investigated by numerical experiments
Many algorithms based on information theoretic measures and/or temporal statistics of the signals ha...
Independent Components Analysis finds a linear transformation to variables which are maximally stati...
Independent Component Analysis (ICA) is a statistical sig-nal processing technique whose main applic...
Independent Component Analysis (ICA) can be studied from different angles. The performance of ICA al...
International audienceWe study optimization methods for solving the maximum likelihood formulation o...
Abstract. This paper derives a new algorithm that performs independent component analysis (ICA) by o...
Recent approaches to independent component analysis (ICA) have used kernel independence measures to ...
Recent approaches to independent component analysis (ICA) have used kernel independence measures to ...
ISBN 978-3-642-15994-7, SoftcoverInternational audienceIn this paper, we consider the Independent Co...
Geometric algorithms for linear independent component analysis (ICA) have recently received some a...
We derive an asymptotic Newton algorithm for Quasi-Maximum Likelihood estimation of the ICA mixture ...
It is seemingly paradoxical to the classical definition of the independent component analysis (ICA),...
Preprint accepted for publication in Neural Computation It is seemingly paradoxical to the classical...
International audienceIn this paper, a new ICA algorithm based on non-polynomial approximation of ne...
Simple linear independent component analysis (ICA) algorithms work efficiently only in linear mixing...
Many algorithms based on information theoretic measures and/or temporal statistics of the signals ha...
Independent Components Analysis finds a linear transformation to variables which are maximally stati...
Independent Component Analysis (ICA) is a statistical sig-nal processing technique whose main applic...
Independent Component Analysis (ICA) can be studied from different angles. The performance of ICA al...
International audienceWe study optimization methods for solving the maximum likelihood formulation o...
Abstract. This paper derives a new algorithm that performs independent component analysis (ICA) by o...
Recent approaches to independent component analysis (ICA) have used kernel independence measures to ...
Recent approaches to independent component analysis (ICA) have used kernel independence measures to ...
ISBN 978-3-642-15994-7, SoftcoverInternational audienceIn this paper, we consider the Independent Co...
Geometric algorithms for linear independent component analysis (ICA) have recently received some a...
We derive an asymptotic Newton algorithm for Quasi-Maximum Likelihood estimation of the ICA mixture ...
It is seemingly paradoxical to the classical definition of the independent component analysis (ICA),...
Preprint accepted for publication in Neural Computation It is seemingly paradoxical to the classical...
International audienceIn this paper, a new ICA algorithm based on non-polynomial approximation of ne...
Simple linear independent component analysis (ICA) algorithms work efficiently only in linear mixing...
Many algorithms based on information theoretic measures and/or temporal statistics of the signals ha...
Independent Components Analysis finds a linear transformation to variables which are maximally stati...
Independent Component Analysis (ICA) is a statistical sig-nal processing technique whose main applic...