A Survey of Sparse Representation: Algorithms and Applications
By Zheng Zhang, Yong Xu, Jian Yang, Xuelong Li, David Dapeng Zhang. IEEE Access, 2015
1,061 citations recorded by the source corpus.
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.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/7fda3b7e173936bc8acbafc7f2963c10675939d8
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Citations in the corpus
- A Survey on Deep Learning for Data-Driven Soft Sensors (2021, background): “To obtain a relatively smaller cost, the probability of neuronal activation should be as small as possible [73].”
- Feature Selection Based on Structured Sparsity: A Comprehensive Study (2017, background): “Some other related works can be found in [34]–[36].”
- Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation (2025, unknown): “Sparse coding serves as a powerful technique for compressing high-dimensional signals and extracting salient features (Wright et al., 2010; Zhang et al., 2015), with learned sparse representations often providing additional computational benefits.”