关键词: 稀疏保持, 局部Fisher判别分析, 半监督降维

Abstract: A kind of algorithm called Sparsity Local Fisher Discriminant Analysis（SLFDA） is proposed, which introduces sparsity preserving projections with trade-off parameter on the basis of local Fisher discriminant analysis for dimensionality reduction, preserving the global geometric structure and local neighborhood information of data in the process of projecting for dimensionality reduction. Experiments operated on UCI datasets and YaleB face dataset show, the algorithm inosculates merits of local Fisher discriminant analysis and sparsity preserving projections； compared with the existing semi-supervised local Fisher discriminant for dimensional reduction, the algorithm can improve the accuracy of classified algorithms based on the shortest Euclidean distance.

Key words: sparsity preserving, local Fisher discriminant analysis, semi-supervised dimensional reduction