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As a dimensionality reduction algorithm, Locality Preserving Projections (LPP) is widely used in machine learning and pattern recognition. In the recognition classification, in order to make better use of the category information, in addition to maintaining the local features of the sample points, the low-dimensional face image information is effectively extracted from the high-dimensional data and the recognition rate and recognition speed of the face image are improved. The classification is optimized to a certain extent. Based on the LPP algorithm combined with the manifold learning idea, a reformation locality preserve projections algorithm (RLPP) is proposed by constructing an attractive vector. After the data set is classified by the extreme learning machine classifier, the experiment is carried out on the standard face database. The experimental results show that the improved algorithm has better recognition rate than the LPP algorithm, the local-preserving average neighborhood margin maximization algorithm and the robustness linear dimensionality reduction algorithm，and has strong generalization ability and high recognition rate.