Based on the sparsity of scene (moving target and few scatterers on the same resolution cell), MTD and 3D imaging are investigated by means of compressed sensing (CS) for airship sparse array radar and airborne three-aperture MMW SAR. Based on the sparsity of continuous scene sparse spectrum, sidelooking 3D imaging is investigated by means of CS for airborne cross-track sparse array SAR. Some s...

Sparse signal recovery addresses the problem of solving underdetermined linear inverse problems subject to a sparsity constraint. We propose a novel prior formulation, the structured spike and slab prior, which allows to incorporate a priori knowledge of the sparsity pattern by imposing a spatial Gaussian process on the spike and slab probabilities. Thus, prior information on the structure of t...

Abstract—Mixing in the hyperspectral imaging occurs due to the low spatial resolutions of the used cameras. The existing pure materials “endmembers” in the scene share the spectra pixels with different amounts called “abundances”. Unmixing of the data cube is an important task to know the present endmembers in the cube for the analysis of these images. Unsupervised unmixing is done with no info...

In many language-related tasks, it would be extremely useful to know the probability that a sentence or word sequence will occur in a document. However, there is not enough data to account for all word sequences. Thus, n-gram models are used to approximate the probability of word sequences. Making an independence assumption between the n-grams reduces some of the problems with data sparsity, bu...

Time-aware recommender systems (TARS) are systems that take into account a time factor the age of the user data. There are three approaches for using a time factor: (1) the user data may be given different weights by their age, (2) it may be treated as a step in a biological process and (3) it may be compared in different time frames to find a significant pattern. This research deals with the l...

Sparsity-inducing norm has been a powerful tool for learning robust models with limited data in high dimensional space. By imposing such norms as constraints or regularizers in an optimization setting, one could bias the model towards learning sparse solutions, which in many case have been proven to be more statistically efficient [Don06]. Typical sparsityinducing norms include `1 norm [Tib96] ...

This paper is concerned with nonlinear inverse problems where data and solution are vector valued and, moreover, where the solution is assumed to have a sparse expansion with respect to a preassigned frame. We especially focus on such problems where the different components of the solution exhibit a common or so–called joint sparsity pattern. Joint sparsity means here that the measure (typicall...

Introduction: Water-fat separation is of interest in several MRI applications including fat suppression and fat quantification. Chemical shift imaging allows robust water-fat separation [1, 2], however the acquisition of multiple images results in prolonged scan time. Accelerated water-fat separation using compressed sensing (CS) was partly addressed in [3] by considering the separation as a sp...

We consider a learning algorithm generated by a regularization scheme with a concave regularizer for the purpose of achieving sparsity and good learning rates in a least squares regression setting. The regularization is induced for linear combinations of empirical features, constructed in the literatures of kernel principal component analysis and kernel projection machines, based on kernels and...

Sparse representation methods have been shown to tackle adequately the inherent speed limits of magnetic resonance imaging (MRI) acquisition. Recently, learning-based techniques have been used to further accelerate the acquisition of 2D MRI. The extension of such algorithms to dynamic MRI (dMRI) requires careful examination of the signal sparsity distribution among the different dimensions of t...