文献类型：期刊文献

英文题名：Low-Rank Representation with Empirical Kernel Space Embedding of Manifolds

作者：Feng, Wenyi[1,4];Wang, Zhe[2,3];Xiao, Ting[2,3]

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

年份：2025

卷号：185

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20250517782031);WOS:【SCI-EXPANDED(收录号:WOS:001413743000001)】；

基金：This work is supported by Natural Science Foundation of China under Grant 62476087, Natural Science Foundation of China under Grant No. 62076094, National Key Research and Development Program of China under Grant 2022YFB3203500.

语种：英文

外文关键词：Low-rank representation; Kernel mapping; Manifold learning; Feature extraction

摘要：Low-Rank Representation (LRR) methods integrate low-rank constraints and projection operators to model the mapping from the sample space to low-dimensional manifolds. Nonetheless, existing approaches typically apply Euclidean algorithms directly to manifold data in the original input space, leading to suboptimal classification accuracy. To mitigate this limitation, we introduce an unsupervised low-rank projection learning method named Low-Rank Representation with Empirical Kernel Space Embedding of Manifolds (LRR-EKM). LRR-EKM leverages an empirical kernel mapping to project samples into the Reproduced Kernel Hilbert Space (RKHS), enabling the linear separability of non-linearly structured samples and facilitating improved low-dimensional manifold representations through Euclidean distance metrics. By incorporating a row sparsity constraint on the projection matrix, LRR-EKM not only identifies discriminative features and removes redundancies but also enhances the interpretability of the learned subspace. Additionally, we introduce a manifold structure preserving constraint to retain the original representation and distance information of the samples during projection. Comprehensive experimental evaluations across various real-world datasets validate the superior performance of our proposed method compared to the state-of-the-art methods. The code is publicly available at https://github.com/ff-raw-war/LRR-EKM.