ISBN 978-3-642-15994-7, SoftcoverInternational audienceIn this paper, we consider the Independent Component Analysis problem when the hidden sources are non-negative (Non-negative ICA). This problem is formulated as a non-linear cost function optimization over the special orthogonal matrix group SO(n). Using Givens rotations and Newton optimization, we developed an effective axis pair rotation method for Non-negative ICA. The performance of the proposed method is compared to those designed by Plumbley and simulations on synthetic data show the efficiency of the proposed algorithm
The authors propose a new solution to the minimization of marginal entropies (ME) in multidimensiona...
Nonnegative matrix factorization (NMF) is known as a parts-based linear representation for nonnegati...
International audienceWe study optimization methods for solving the maximum likelihood formulation o...
ISBN 978-3-642-15994-7, SoftcoverInternational audienceIn this paper, we consider the Independent Co...
Abstract. This paper derives a new algorithm that performs independent component analysis (ICA) by o...
The performance of ICA algorithms significantly depends on the choice of the contrast function and t...
Independent Component Analysis (ICA) can be studied from different angles. The performance of ICA al...
The independent component analysis (ICA) problem originates from many practical areas, but there has...
International audienceIn this paper, a new ICA algorithm based on non-polynomial approximation of ne...
ABSTRACT: The independent component analysis of a random vector consists of finding for a linear tra...
The Independent Component Analysis (ICA) of a random vector consists of searching for the linear tra...
International audienceIndependent Component Analysis (ICA) is a well-known technique for solving bli...
Matrix optimization of cost functions is a common problem. Construction of methods that enable each ...
We explore the use of geometrical methods to tackle the non-negative independent component analysis...
Simple linear independent component analysis (ICA) algorithms work efficiently only in linear mixing...
The authors propose a new solution to the minimization of marginal entropies (ME) in multidimensiona...
Nonnegative matrix factorization (NMF) is known as a parts-based linear representation for nonnegati...
International audienceWe study optimization methods for solving the maximum likelihood formulation o...
ISBN 978-3-642-15994-7, SoftcoverInternational audienceIn this paper, we consider the Independent Co...
Abstract. This paper derives a new algorithm that performs independent component analysis (ICA) by o...
The performance of ICA algorithms significantly depends on the choice of the contrast function and t...
Independent Component Analysis (ICA) can be studied from different angles. The performance of ICA al...
The independent component analysis (ICA) problem originates from many practical areas, but there has...
International audienceIn this paper, a new ICA algorithm based on non-polynomial approximation of ne...
ABSTRACT: The independent component analysis of a random vector consists of finding for a linear tra...
The Independent Component Analysis (ICA) of a random vector consists of searching for the linear tra...
International audienceIndependent Component Analysis (ICA) is a well-known technique for solving bli...
Matrix optimization of cost functions is a common problem. Construction of methods that enable each ...
We explore the use of geometrical methods to tackle the non-negative independent component analysis...
Simple linear independent component analysis (ICA) algorithms work efficiently only in linear mixing...
The authors propose a new solution to the minimization of marginal entropies (ME) in multidimensiona...
Nonnegative matrix factorization (NMF) is known as a parts-based linear representation for nonnegati...
International audienceWe study optimization methods for solving the maximum likelihood formulation o...