Dictionary Training with Genetic Algorithm for Sparse Representation
Recently, Dozens of applications for sparse representation has been developed. The model with / norm as constraint is an NP hard problem. How to find the global optimal solution is a difficult point of this area. For genetic algorithm is good at solving NP hard problem, a dictionary training method based on it is proposed in this paper. The samples are first classified randomly for generate original population and residual of approximate the sample class with a rank-1 matrix as fitness is calculated. Then, select better individuals using league matches. After that new individuals are generated from crossover and mutation and the residual of the representation is used as data samples for training the dictionary for the next layer. The experimental results show the algorithm are effective.
dictionary sparse representation genetic algorithm SVD
Zhiguo Chang Jian Xu
School of Information Engineering Changan University Shannxi Road Traffic Intelligent Detection and School of Communication and Information Engineering Xian University of Posts & Telecommunications S
国际会议
西安
英文
444-447
2011-05-27(万方平台首次上网日期,不代表论文的发表时间)