This article proposes a stochastic version of the matching pursuit algorithm for Bayesian variable selection in linear regression. In the Bayesian formulation, the prior distributi...
— The Bath University Matching Pursuit (BUMP) project aims at developing new matching pursuit (MP) algorithms for still image and video compression. Compared to traditional MP co...
The two major approaches to sparse recovery are L1-minimization and greedy methods. Recently, Needell and Vershynin developed Regularized Orthogonal Matching Pursuit (ROMP) that ha...
We analyse matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sample compression scheme. We show that this bound...
— A progressive and scalable, region of interest (ROI) image coding scheme based on matching pursuits (MP) is presented. Matching pursuit is a multi-resolutional signal analysis ...
Pseudo-periodicity is one of the basic job arrival patterns on data-intensive clusters and Grids. In this paper, a signal decomposition methodology called matching pursuit is appl...
Hui Li, Richard Heusdens, Michael Muskulus, Lex Wo...
Recently, significant attention in compressed sensing has been focused on Basis Pursuit, exchanging the cardinality operator with the l1-norm, which leads to a linear formulation...
Christian R. Berger, Javier Areta, Krishna R. Patt...
Abstract. Polytope Faces Pursuit (PFP) is a greedy algorithm that approximates the sparse solutions recovered by 1 regularised least-squares (Lasso) [4,10] in a similar vein to (Or...
We propose a variant of Orthogonal Matching Pursuit (OMP), called LoCOMP, for scalable sparse signal approximation. The algorithm is designed for shift-invariant signal dictionari...