中文摘要：

为了增强高维数据在低维子空间中的模式识别能力，假设任意2个类别相同的相似样本其稀疏表示也相似，并基于SPP和LPP思想，提出一种可鉴别稀疏保局投影降维新方法DSLPP．该方法通过稀疏表示学习和保局部投影，使得在投影子空间中不仅能够保持稀疏表示对数据很好的表达能力，而且较好地获取高维数据所蕴含的本质局部流形结构和自然判别信息，从而增强高维数据在子空间中的表示能力和可鉴别能力．在3个典型的人脸数据集Yale，ORL和PIE29上，将所提出方法DSLPP与PCA，LPP，NPE和sPP进行对比试验．结果表明DSLPP是一种有效的降维方法，能够较好地改善高维数据在低维子空间中的分类效果．

英文摘要：

To improve the pattern recognition for high-dimensional data, assuming that any two similar samples within the same class had similar sparse representations, a novel dimensionality reduction method of discriminative sparsity locality preserving projections （DSLPP） was proposed based on SPP and LPP. Through sparse learning and locality preserving projections, the good sparse representation was preserved by the proposed DSLPP, and the potential local manifold structure and the discrimination information of high-dimensional data were also be well captured in the obtained subspace. The expression ability and the identifiability of high dimensional data were enhanced in the subspace. The experiments were completed on Yale, ORL and PIE29 face databases to compare DSLPP with PCA, LPP, NPE and SPP. The results show that the proposed DSLPP is an effective dimensionality reduction algorithm, and it can well improve the classification performance for high-dimensional data in subspace.