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In this article, we present a new tool for sparse coding : Multivariate DLA which empirically learns the characteristic patterns associated to a multivariate signals set. Once learned, Multivariate OMP approximates sparsely any signal of this considered set. These methods are specified to the 2D rotation-invariant case. Shift and rotation-invariant cases(More)
We propose a frequency blind deconvolution algorithm based on mutual information rate as a measure of whiteness. In the case of seismic data, the algorithm of Wiggins [11] based on kurtosis, which is a supergaussianity criterion, is often used. We study the robustness in noisy context of these two algorithms, and compare them with Wiener filtering. We(More)
In this paper, a new blind single-input single-output (SISO) deconvolution method based on the minimization of the mutual information rate of the deconvolved output is proposed. The method works in the frequency domain and requires estimation of the signal probability density function. Thus, the algorithm uses higher order statistics (except for Gaussian(More)
Non-negative blind source separation (BSS) has raised interest in various fields of research, as testified by the wide literature on the topic of non-negative matrix factorization (NMF). In this context, it is fundamental that the sources to be estimated present some diversity in order to be efficiently retrieved. Sparsity is known to enhance such contrast(More)
In this letter, a review of the quaternionic least mean squares (QLMS) algorithm is proposed. Three versions coming from three derivation ways exist: the original QLMS [1] based on componentwise gradients, HR-QLMS [2] based on a quaternion gradient operator and iQLMS [3] based on an involutions-gradient. Noting and investigating the differences between the(More)
In this work, we study Non-Negative Matrix Factorization (NMF) and compare standard algorithms with an extension to NMF of a Blind Source Separation algorithm using sparsity, Generalized Morphological Component Analysis (GMCA). We also develop a more robust version of GMCA handling more precisely the priors through sub-iterations, which we call rGMCA. We(More)
This article addresses the issue of learning efficient linear spatial filters and a classification function to match noninvasive electroencephalographic (EEG) signals to motor imagery tasks voluntarily performed by the subjects. The new perspective used in this article consists in releasing the widely accepted hypothesis stating that motor tasks-related(More)