Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55932
Title: A survey of sparse representation : algorithms and applications
Authors: Zhang, Z
Xu, Y
Yang, J
Li, X
Zhang, D 
Keywords: Dictionary learning
Sparse representation
Compressive Sensing
Greedy algorithm
Constrained optimization
Proximal algorithm
Homotopy algorithm
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2015, v. 3, p. 490-530 How to cite?
Journal: IEEE access 
Abstract: Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision, and pattern recognition. Sparse representation also has a good reputation in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. The main purpose of this paper is to provide a comprehensive study and an updated review on sparse representation and to supply guidance for researchers. The taxonomy of sparse representation methods can be studied from various viewpoints. For example, in terms of different norm minimizations used in sparsity constraints, the methods can be roughly categorized into five groups: 1) sparse representation with l0-norm minimization; 2) sparse representation with lp-norm (0 <; p <; 1) minimization; 3) sparse representation with l1-norm minimization; 4) sparse representation with l2,1-norm minimization; and 5) sparse representation with l2-norm minimization. In this paper, a comprehensive overview of sparse representation is provided. The available sparse representation algorithms can also be empirically categorized into four groups: 1) greedy strategy approximation; 2) constrained optimization; 3) proximity algorithm-based optimization; and 4) homotopy algorithm-based sparse representation. The rationales of different algorithms in each category are analyzed and a wide range of sparse representation applications are summarized, which could sufficiently reveal the potential nature of the sparse representation theory. In particular, an experimentally comparative study of these sparse representation algorithms was presented.
URI: http://hdl.handle.net/10397/55932
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2015.2430359
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