一种进行稀疏编码的复数数据词典快速训练方法  

Fast Complex Valued Dictionary Learning Method for Sparse Representation

作　　者：郝红星[1,2] 吴玲达[1,2] 黄为[1] 

机构地区：[1]国防科学技术大学信息系统与管理学院,湖南长沙410073 [2]装备学院复杂电子系统仿真重点实验室,北京101400

出　　处：《软件学报》2015年第8期1960-1967,共8页Journal of Software

基　　金：湖南省研究生科研创新项目(CX2011B025);国防科学技术大学博士生创新资助项目(B110503)

摘　　要：稀疏编码理论应用于信号处理的各个领域,为了获取优化的稀疏编码,需要通过训练获取数据词典.提出了一种复数域数据词典的快速训练方法,将词典训练问题转化为最优化问题并交替地对词典原子和编码进行最优化而得到最终训练词典.在对词典原子的最优化过程中,采用具有记忆性的在线训练算法;而在对编码进行最优化的过程中,采用交换乘子方向方法进行实现.通过实验得出:所提出的算法能够有效地提高数据词典的训练效率,在保证收敛值的同时缩短训练时间,并且对于训练样本中的噪声具有鲁棒性.Sparse representation is widely used in signal processing. The best representation is based on the adaptive dictionary that trained from the processing data. This paper proposes a new complex valued dictionary learning method which turns the dictionary learning into an optimization problem and performs the optimization on the dictionary atoms and coding alternately. An online training method with memory is used in the optimization on the dictionary atoms, and an insurance of alternated direction method of multipliers is solved in the optimization on the coding. The proposed algorithm is proved to be of high efficiency, minimizing the training time while converging to the optimized value. The presented method is also robust to the noise in the training set.

关 键 词：复数词典训练 在线学习 交换乘子方向方法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]