文献类型：期刊文献

英文题名：Discriminative sparse subspace learning with manifold regularization

作者：Feng, Wenyi[1,2,3];Wang, Zhe[1,2];Cao, Xiqing[4];Cai, Bin[4];Guo, Wei[1,2];Ding, Weichao[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[3]Qinghai Univ, Informat Technol Ctr, Xining 810016, Peoples R China;[4]Shanghai Aerosp Control Technol Inst, Shanghai 201109, Peoples R China

年份：2024

卷号：249

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20241415837020);WOS:【SCI-EXPANDED(收录号:WOS:001219272300001)】；

基金：This work is supported by National Key Research and Development Program of China under Grant No. 2022YFB3203500, Natural Science Foundation of China under Grant No. 62076094, the Fundamental Research Funds for the Central Universities, Shanghai Science and Technology Program "Federated based cross-domain and cross-task incremental learning"under Grant No. 21511100800.

语种：英文

外文关键词：Manifold regularization; Subspace learning; Linear classification; Sparse constraint

摘要：Common subspace learning methods only utilize local or global structure in feature extraction, and cannot obtain the global optimal discriminative projection matrix. For this reason, this paper proposes a discriminative sparse subspace learning method based on the manifold regularization framework (DSSL-MR), which introduces the graph Laplacian matrix that reflects the intrinsic geometric structure of the sample as a penalty term. DSSL-MR simultaneously uses both sub -manifold and multi -manifold information of samples for obtaining optimal projection to enhance the discriminability of different classes in subspace. DSSL-MR uses the sparse property of the L 21 -norm to constrain the projection matrix, which can eliminate redundant features and select features that are significant for classification. It is a linear supervised method, which belongs to the Fisher discriminant analysis framework. Experimental results on multiple real -world datasets show that the algorithm is very effective in classification and has high recognition rates.