摘要
多测量向量的联合稀疏重构要求多个源信号共享相同的稀疏结构,但实际应用中较难得到具有完全相同的稀疏结构的测量信号。为了降低非共享稀疏结构对MMV模型联合稀疏重构的影响,文中提出了一种改进贪婪类联合稀疏重构算法的方法。该方法在每次迭代时并不要求各测量向量选择相同的表示原子,而是要求选择同一类的表示原子。改进后的算法可用于非共享多测量向量的稀疏表示分类。基于模拟数据和标准人脸库数据的实验结果表明,改进后的模型可有效提高稀疏表示的分类性能。
Simultaneous sparse reconstruction of multiple measurement vectors(MMV)requires that the multiple measurement signals share the same sparse structure.However,it is difficult to get the measurement signals exactly sharing same sparse structure in practical applications.In order to reduce the influence of non-shared sparse structure on simultaneous sparse reconstruction of MMV model,this paper proposed a method to improve simultaneous sparse reconstruction algorithms belonging to greedy series.At each iteration,the method does not require that each measurement vector chooses the same representation atoms,but requires selecting representation atoms in the same class.The improved algorithm can be used for sparse representation classification of non-shared multiple measurement vectors.Experiments on simulated data and standard face database show that the improved model can effectively improve the performance of sparse representation classification.
出处
《计算机科学》
CSCD
北大核心
2018年第3期258-262,共5页
Computer Science
基金
国家自然科学基金(61662024
61762037)
江西省教育厅项目(GJJ160512)
江西省普通本科高校中青年教师发展计划访问学者专项资金项目(赣教办函[2016]109号)
南昌市传感器网络和压缩感知知识创新团队(2016T75)资助
关键词
压缩感知
多测量向量
共享稀疏结构
稀疏表示分类
Compressed sensing
Multiple measurement vector
Shared sparse structure
Sparse representation classification
