基于核稀疏保持投影的典型相关分析算法

模式识别的技术核心就是特征提取，而特征融合则是对特征提取方法的强力补充，对于提高特征的识别效率具有重要作用。本文基于稀疏表示方法，将稀疏表示方法用到高维度空间，并利用核方法在高维度空间进行稀疏表示，用其计算核稀疏表示系数，同时研究了核稀疏保持投影算法(Kernel sparsity preserve projection,KSPP)。将KSPP引入到典型相关分析算法(Canonical correlation analysis,CCA)，研究了基于核稀疏保持投影的典 型相关分析算法(Kernel sparsity preserve canonical correlation analysis,K-SPCCA)。在多特征手写体数据库和人脸图像数据库上分别证实了本文提出方法的可靠性和有效性 。

Canonical Correlation Analysis Algorithm Based on Kernel Sparsity Preserve Projection

The key of pattern recognition is feature extraction. Fusion of feature is an important complement of feature extraction, and it has been proved to be important to improve discrimination. Here, the sparse representation method is studied by introducing sparse representation into a high dimensional feature space and utilizing kernel trick to make sparse representation in the space.The kernel sparse representation coefficients with kernel sparse representation are utilized, then kernel sparsity preserve projection (KSPP) subspace. Moreover KSPP is brought into canonical correlation analysis (CCA), then kernel sparsity preserve canonical correlation analysis (KSPCCA) is studied. The proposed algorithm is reliable and validated on the multiple feature database and face database.