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.