In this work, we deal with blind source separation of a class of nonlinear mixtures. The proposed method can be regarded as an adaptation of the solutions developed in [1, 2] to th...
Functional Data Analysis (FDA) is used for datasets that are more meaningfully represented in the functional form. Functional principal component analysis, for instance, is used to...
In the paper we present new Alternating Least Squares (ALS) algorithms for Nonnegative Matrix Factorization (NMF) and their extensions to 3D Nonnegative Tensor Factorization (NTF) ...
In this paper, we propose to use the Huber M-estimator cost function as a contrast function within the complex FastICA algorithm of Bingham and Hyvarinen for the blind separation o...
Abstract. This paper deals with the problem of Blind Source Separation. Contrary to the vast majority of works, we do not assume the statistical independence between the sources an...
This paper describes a new blind source separation method for instantaneous linear mixtures. This new method coined GMCA (Generalized Morphological Component Analysis) relies on mo...
We consider Independent Component Analysis (ICA) for the case of binary sources, where addition has the meaning of the boolean “Exclusive Or” (XOR) operation. Thus, each mixtur...
Abstract. Renyi’s entropy-based criterion has been proposed as an objective function for independent component analysis because of its relationship with Shannon’s entropy and i...
Abstract. Visual perception of transformation invariance, such as translation, rotation and scaling, is one of the important functions of processing visual information in the Brain...
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can b...