基于稀疏表示和非参数判别分析的降维算法

针对人脸识别问题提出一种新的监督降维算法。算法首先基于稀疏表示理论,利用同类样本间的稀疏重构来构建图。此方案不仅可以克服传统图构造方法中参数选择的困难,而且能够更好地刻画类内信息。然后,算法采用非参数类间离差来刻画类间信息,非参数类间离差在处理复杂分布数据时相比于参数类间离差更具判别力。最后,算法通过保持类内稀疏重构关系的同时最大化非参数类间离差来求得最优的投影矩阵。在ORL和Extended Yale B公共人脸数据库的实验表明,该算法能够获得较好的识别结果。

Dimensionality reduction based on sparse representation and nonparametric discriminant analysis

Aiming at the face recognition problem, a new supervised dimensionality reduction algorithm is presented. On the basis of sparse representation theory, the proposed algorithm uses the within-class sparse construction to construct graph. This scheme can avoid the difficulty of parameter selection in traditional graph construction methods, and characterize the within-class information well. Furthermore, the multi-class nonparametric discriminant scatter is applied to characterize the between-class information, which will be more discriminative than parametric discriminant scatter in dealing with complex-distributed data. By maximizing the nonparametric between-class scatter and preserving the within-class sparse reconstructive relationship, the proposed algorithm can seek for the optimal projection matrix. Experimental results on ORL and Extended Yale B dataset show that the proposed method can achieve good recognition effect.