计算机工程与应用2012，Vol.48Issue(4)：173-175,3.DOI:10.3778/j.issn.1002-8331.2012.04.051
稀疏局部Fisher判别分析
Sparsity local Fisher discriminant analysis
摘要
Abstract
A kind of algorithm called Sparsity Local Fisher Discriminant Analysis (SLFD A) 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.关键词
稀疏保持/局部Fisher判别分析/半监督降维Key words
sparsity preserving/ local Fisher discriminant analysis/ semi-supervised dimensional reduction分类
信息技术与安全科学引用本文复制引用
许淑华,齐鸣鸣..稀疏局部Fisher判别分析[J].计算机工程与应用,2012,48(4):173-175,3.基金项目
国家自然科学基金(No.10871226):浙江省教育厅科研项目(No.Y201018654). （No.10871226）
