With the development of Web, users spend more time accessing information that they seek. As a result, recommendation systems have emerged to provide with preferred contents by filtering abundant information, along providing means exposing search results effectively. These operate based on user reactions items or various item features. It is known sparse datasets are less reliable because recomm...

Date of publication: 13 June 2014 T his article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results, 2) sparsity-based methods for wide-angle SAR imaging and anisotropy characteriz...

This paper presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. In particular, it reviews (i) analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results; (ii) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization; (iii) sparsity-based methods fo...

We propose a new sparsity-smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness is crucial for mathematical theory as well as performance for finite-sample data. We present a computationally efficient algorithm, with provable numerical convergence properties, for optimizing the penalized likelihood. Furthermore, we provide oracle results...

Diffuse optical tomography is a novel molecular imaging technology for small animal studies. Most known reconstruction methods use the diffusion equation (DA) as forward model, although the validation of DA breaks down in certain situations. In this work, we use the radiative transfer equation as forward model which provides an accurate description of the light propagation within biological med...

The theory of compressive sensing (CS) asserts that an unknown signal x ∈ CN can be accurately recovered from m measurements with m N provided that x is sparse. Most of the recovery algorithms need the sparsity s = ‖x‖0 as an input. However, generally s is unknown, and directly estimating the sparsity has been an open problem. In this study, an estimator of sparsity is proposed by using Bayesia...

An Outlier Mining Algorithm Based on Constrained Concept Lattice Jifu Zhang , Sulan Zhang , Kai H. Chang b, and Xiao Qin a School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, P. R. China 030024 b Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA 36849-5347 [email protected] Abstract: Traditional outlier min...

Case-based reasoning involves reasoning from cases: speciic pieces of experience, the reasoner's or another's, that can be used to solve problems. We use the term \graph-structured" for representations that (1) are capable of expressing the relations between any two objects in a case, (2) allow the set of relations used to vary from case to case, and (3) allow the set of possible relations to b...