The small sample size and the loss of effective dimension problems always exist in discriminative dimension reduction methods of high-dimensional data classification. To address these problems, a Sample Locality Preserving Discriminant Analysis (SLPDA) method is proposed by integrating the latest patch alignment framework and Locality Preserving Projections (LPP). The within-class and out-class neighborhood relationships of all samples in the SLPDA are constructed by summing the within-class and out-class neighborhood graphs of each sample, respectively. Thereafter, the optimal mapping from a high-dimensional input space to a low-dimensional feature space of the SLPDA is obtained by making the within-class neighbors of all samples as close as possible and meanwhile keeping the out-class neighbors as distant as possible. The proposed SLPDA method avoids the small sample size problem of high-dimensional data classification and extends the effective dimension of low-dimensional feature space. Experimental results on several high-dimensional face databases, e. G. ORL, FERET and PIE, indicate that the proposed SLPDA method significantly outperforms the classical discriminative dimension reduction methods. Comparing with Discriminative Locality Alignment(DLA), which is also a dimension reduction method based on patch alignment framework, the recognition rate of SLPDA on a FERET subset is 4. 5% higher.%针对高维数据分类中鉴别特征降维方法的小样本问题和有效维度丢失问题,结合最新提出的片对齐框架和保局投影提出了样本保局鉴别分析方法.该方法通过分别构造每个样本的类内近邻图和类外近邻图,并将所有样本的类内近邻图和类外近邻图结合起来,形成了所有样本的类内近邻和类外近邻关系.然后,在使所有样本的类内近邻尽可能地聚集在一起的同时使类外近邻尽可能地分开,得到从高维输入空间到低维特征空间的最优映射关系.该方法有效避免了高维数据分类的小样本问题且扩展了鉴别分析的低维特征空间的有效维度.在ORL、FERET和PIE等人脸库上的高维数据分类实验证实,样本保局鉴别分析方法显著优于经典的鉴别特征降维方法.与基于片对齐框架提出的鉴别局部对齐方法相比,样本保局鉴别分析方法在FERET库上的分类识别精度提高了4.5％以上.
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