计算机工程与应用2011，Vol.47Issue(29)：186-188,3.DOI:10.3778/j.issn.1002-8331.2011.29.052
监督型稀疏保持投影
Supervised sparsity preserving projections
摘要
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 ex tending 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.关键词
稀疏保持投影/线性判别分析/降维/人脸识别Key words
sparsity preserving projection/ linear discriminant analysis dimensionality reduction/ face recognition分类
信息技术与安全科学引用本文复制引用
相文楠,赵建立..监督型稀疏保持投影[J].计算机工程与应用,2011,47(29):186-188,3.基金项目
山东省自然科学基金(No.ZR2010FL011). （No.ZR2010FL011）
