A Survey of Sparse Representation: Algorithms and Applications
Résumé
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:
- sparse representation with $l_0$-norm minimization;
- sparse representation with $l_p$-norm ($0 < p < 1$) minimization;
- sparse representation with $l_1$-norm minimization;
- sparse representation with $l_{2,1}$-norm minimization; and
- sparse representation with $l_2$-norm minimization.
- greedy strategy approximation;
- constrained optimization;
- proximity algorithm-based optimization; and
- homotopy algorithm-based sparse representation.
Origine : Fichiers produits par l'(les) auteur(s)
Loading...