一种新的有监督的局部保持典型相关分析算法

从模式识别的角度出发,在局部保持典型相关分析的基础上,提出一种有监督的局部保持典型相关分析算法(SALPCCA)。该方法在构造样本近邻图时将样本的类别信息考虑在内,由样本间的距离度量确定权重,建立样本间的多重权重相关,通过使同类内的成对样本及其近邻间的权重相关性最大,从而能够在利用样本的类别信息的同时,也能保持数据的局部结构信息。此外,为了能够更好地提取样本的非线性信息,将特征集映射到核特征空间,又提出一种核化的SALPCCA(KSALPCCA)算法。在ORL、Yale、AR等人脸数据库上的实验结果表明,该方法较其他的传统典型相关分析方法有着更好的识别效果。

A new supervised locality preserving canonical correlation analysis algorithm

From the angle of model recognition, based on Canonical Correlation Analysis (CCA) we propose a new supervised locality preserving canonical correlation analysis (SALPCCA) based on the ALPCCA. By leveraging the useful information of class label, we can expediently construct the nearest neighbor graph and build multi-weighted correlation between samples. Through maximizing the weighted correlation between corresponding samples and their near neighbors belonging to the same classes, the SALPCCA effectively utilizes the class label information and preserves the local manifold structure of the data. Besides, we also propose a kernel SALPCCA (KSALPCCA) based on the kernel methods to better extract the nonlinear features of the data. The experimental results on the face databases of ORL, Yale and AR show that the proposed algorithm has better performance compared with the traditional canonical correlation analysis methods.