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
[[abstract]]Most independent component analysis methods for blind source separation rely on the fund...
Matrix optimization of cost functions is a common problem. Construction of methods that enable each ...
Independent component analysis is intended to recover the mutually independent components from their...
ISBN 978-3-642-15994-7, SoftcoverInternational audienceIn this paper, we consider the Independent Co...
International audienceIndependent Component Analysis (ICA) is a well-known technique for solving bli...
The performance of ICA algorithms significantly depends on the choice of the contrast function and t...
Abstract. This paper derives a new algorithm that performs independent component analysis (ICA) by o...
The Independent Component Analysis (ICA) of a random vector consists of searching for the linear tra...
International audienceIn this paper, a new ICA algorithm based on non-polynomial approximation of ne...
The independent component analysis (ICA) problem originates from many practical areas, but there has...
Independent Component Analysis (ICA) can be studied from different angles. The performance of ICA al...
A fundamental problem in machine learning research, as well as in many other disciplines, is finding...
International audienceWe study optimization methods for solving the maximum likelihood formulation o...
International audienceIndependent Component Analysis (ICA) is a technique for unsupervised explorati...
Nonnegative matrix factorization (NMF) is known as a parts-based linear representation for nonnegati...
[[abstract]]Most independent component analysis methods for blind source separation rely on the fund...
Matrix optimization of cost functions is a common problem. Construction of methods that enable each ...
Independent component analysis is intended to recover the mutually independent components from their...
ISBN 978-3-642-15994-7, SoftcoverInternational audienceIn this paper, we consider the Independent Co...
International audienceIndependent Component Analysis (ICA) is a well-known technique for solving bli...
The performance of ICA algorithms significantly depends on the choice of the contrast function and t...
Abstract. This paper derives a new algorithm that performs independent component analysis (ICA) by o...
The Independent Component Analysis (ICA) of a random vector consists of searching for the linear tra...
International audienceIn this paper, a new ICA algorithm based on non-polynomial approximation of ne...
The independent component analysis (ICA) problem originates from many practical areas, but there has...
Independent Component Analysis (ICA) can be studied from different angles. The performance of ICA al...
A fundamental problem in machine learning research, as well as in many other disciplines, is finding...
International audienceWe study optimization methods for solving the maximum likelihood formulation o...
International audienceIndependent Component Analysis (ICA) is a technique for unsupervised explorati...
Nonnegative matrix factorization (NMF) is known as a parts-based linear representation for nonnegati...
[[abstract]]Most independent component analysis methods for blind source separation rely on the fund...
Matrix optimization of cost functions is a common problem. Construction of methods that enable each ...
Independent component analysis is intended to recover the mutually independent components from their...