基于特征提取矩阵的稀疏系数求解算法

在压缩感知算法的基础上,提出了在字典学习算法过程中同时训练得到一个投影矩阵,通过该矩阵可以直接运算求取稀疏系数的方法.字典训练过程采用的是KSVD字典学习算法,并与传统的L1范数求解算法进行比较,通过实验可知,该方法比传统利用贪婪法等L1算法具有更加快速、识别率更高的特点,提出的算法通过矩阵运算可以直接求解出系数项,而后者则是一个NP问题,需要利用迭代算法来求解,这样对于大样本的测试来说提出的算法具有更好的应用空间,节约的时间将非常显著.

Solving algorithm of sparse coefficient based on feature extraction matrix

On the basis of the compression sensing algorithm,this paper proposes to train a projection matrix in the process of dictionary learning algorithm,through which the method can obtain the sparse coefficient directly.The dictionary training process is based on the KSVD dictionary learning algorithm and is compared with the traditional L1 norm solving algorithm.It can be seen from the experiment that the method has more rapid and higher recognition rate than the traditional L1 algorithm using greedy method.The algorithm can solve the coefficient term directly through the matrix operation,while the latter is an NP problem,which needs to be solved by the iterative algorithm.For the large sample test,the proposed algorithm has better application space,and the time of saving will very noticeable.