期刊名称：International Journal of Future Generation Communication and Networking
印刷版ISSN：2233-7857
出版年度：2016
卷号：9
期号：10
页码：199-208
出版社：SERSC
摘要：Signal sparse matrix structure, the degree of relationship between signal sparse representation, which affect application of compression perception to the effect of recovery reconstruction for signal. In order to solve this problem, a variety of dictionary learning algorithm such as KSVD, OLM (Online dictionary learning method) should be put forward. These algorithms used overlapping image blocks to build a dictionary, produced a large number of sparse coefficients, resulting in a fitting and calculation too slowly, and cannot ensure convergence. Based on this, it designed a fast dictionary learning algorithm based on proximal gradient. Algorithm based on the analysis of proximal gradient multiple, on the basis of convex optimization problem, applied to the dictionary learning involved in solving optimization, reduce the complexity of each iteration, reduces the iterative overhead, at the same time to ensure the convergence. Experiments on synthetic data show that the proposed algorithm dictionary learning speed, the time is short, and obtain a better dictionary.
关键词：The dictionary to learn; Sparse representation; The proximal gradient; ;Global convergence.
