Sparse and structured signal expansions on dictionaries can be obtained through explicit modeling in the coefficient domain. The originality of the present contribution lies in the construction and the study of generalized shrinkage operators, whose goal is to identify structured significance maps. These generalize Group LASSO and the previously introduced Elitist LASSO by introducing more flex...

This paper introduces an approach to estimation in possibly sparse data sets using shrinkage priors based upon the class of hypergeometric-beta distributions. These widely applicable priors turn out to be a four-parameter generalization of the beta family, and are pseudo-conjugate: they cannot themselves be expressed in closed form, but they do yield tractable moments and marginal likelihoods w...

Sparse regression often uses `p norm priors (with p < 2). This paper demonstrates that the introduction of mixed-norms in such contexts allows one to go one step beyond in signal models, and promote some different, structured, forms of sparsity. It is shown that the particular case of the `1,2 and `2,1 norms leads to new group shrinkage operators. Mixed norm priors are shown to be particularly ...

Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image and video. Sparse coding has increasing attraction for image classification applications in recent years. But in the cases where we have some similar images from different classes, such as face recognition applications, different images may be classified into the same class, and hen...

The dual Phase-I algorithm using the most-obtuse-angle row pivot rule is very efficient for providing a dual feasible basis, in either the classical or the basisdeficiency-allowing context. In this paper, we establish a basis-deficiency-allowing Phase-I algorithm using the so-called most-obtuse-angle column pivot rule to produce a primal (deficient or full) basis. Our computational experiments ...

Recently, Spatial Pyramid Matching (SPM) with nonlinear coding strategies, e.g., sparse code based SPM (ScSPM) and locality-constrained linear coding (LLC), have achieved a lot of success in image classification. Although these methods achieve a higher recognition rate and take less time for classification than the traditional SPM, they consume more time to encode each local descriptor extracte...

⎯A novel and efficient speckle noise reduction algorithm based on Bayesian contourlet shrinkage using contourlet transform is proposed. First, we show the sub-band decompositions of SAR images using contourlet transforms, which provides sparse representation at both spatial and directional resolutions. Then, a Bayesian contourlet shrinkage factor is applied to the decomposed data to estimate th...

In a new approach to the development of sparse codes, the programmer de nes a particular algorithm on dense matrices which are actually sparse. The sparsity of the matrices as indicated by the programmer is only dealt with at compile-time. The compiler selects appropriate compact data structure and automatically converts the algorithm into code that takes advantage of the sparsity of the matric...