中文摘要：

基于稀疏表示的分类方法由于其所具有的简单性和有效性获得了研究者的广泛关注，然而如何建立字典原子与类别信息间的联系仍然是一个重要的问题，与此同时大部分稀疏表示分类方法都需要求解受范数约束的优化问题，使得分类任务的计算较复杂。为解决上述问题，该文提出一种新的基于Fisher约束的字典对学习方法。新方法联合学习结构化综合字典和结构化解析字典，然后通过样本在解析字典上的映射直接求解稀疏系数矩阵；同时采用Fisher判别准则编码系数使系数具有一定的判别性。最后将新方法应用到图像分类中，实验结果表明新方法在提高分类准确率的同时还大大降低了计算复杂度，相较于现有方法具有更好的性能。

英文摘要：

Classification method based on sparse representation has won wide attention because of its simplicity and effectiveness, while how to adaptively build the relationship between dictionary atoms and class labels is still an important open question, at the same time most of the sparse representation classification methods need to solve a norm constraint optimization problem, which increases the computational complexity in the classification task. To address this issue, this paper proposes a novel Fisher constraint dictionary pair leaxning method to jointly learn a structured synthesis dictionary and a structured analysis dictionary, then directly obtains the sparse coefficient matrix by analysis dictionary. In this paper, the Fisher criterion is used to encode the coefficients. Finally the new method is applied to image classification task, the experimental results show that the new method not only improves the accuracy of classification but also greatly reduces the computational complexity. Compared with the existing methods, the new method has better performance.