Sparse approximate solutions to linear equations are classically obtained via L1 norm
regularized least squares, but this method often underestimates the true solution. As an …

We address the sparse signal recovery problem in the context of multiple measurement
vectors (MMV) when elements in each nonzero row of the solution matrix are temporally …

Sparse Bayesian learning (SBL) has become a popular approach for direction-of-arrival
(DOA) estimation, but its computational complexity for Bayesian inference is quite high …

A variety of practical methods have recently been introduced for finding maximally sparse
representations from overcomplete dictionaries, a central computational task in compressive …

Neural networks can be compressed to reduce memory and computational requirements, or
to increase accuracy by facilitating the use of a larger base architecture. In this paper we …

This paper addresses the estimation of the latent dimensionality in nonnegative matrix
factorization (NMF) with the (β)--divergence. The (β)-divergence is a family of cost functions …

Regularization and Bayesian methods for system identification have been repopularized in
the recent years, and proved to be competitive wrt classical parametric approaches. In this …

The computationally prohibitive multi-dimensional searching procedure greatly restricts the
application of the maximum likelihood (ML) direction-of-arrival (DOA) estimation method in …

In image processing, sparse coding has been known to be relevant to both variational and
Bayesian approaches. The regularization parameter in variational image restoration is …

The iterations of many sparse estimation algorithms are comprised of a fixed linear filter
cascaded with a thresholding nonlinearity, which collectively resemble a typical neural …