关键词: 稀疏保持投影, 线性判别分析, 降维, 人脸识别

Abstract: Sparsity Preserving Projection（SPP） is a recently proposed unsupervised dimensionality reduction method，thus fails to use the supervised information provided by the labeled data.To address this issue，two supervised algorithms for extending SPP are presented，called SPP-based Linear Discriminant Analysis（SPP+LDA） and supervised SPP（S2PP） respectively.The former takes advantage of sparse reconstructive relationship and label information in data by applying LDA in the SPP transformed subspace，and the latter naturally incorporates discrimination information by utilizing label information to modify sparse reconstructive graph constructed via SPP.The advantages and disadvantages of the two proposed methods are analyzed.The feasibility and effectiveness of the proposed methods are verified on two popular face databases（Yale and AR） with promising results.

Key words: sparsity preserving projection, linear discriminant analysis, dimensionality reduction, face recognition