文献类型：会议论文

英文题名：Robust Structured Sparse Subspace Clustering with Neighborhood Preserving Projection

作者：Feng, Wenyi[1,2,3];Guo, Wei[1,2];Xiao, Ting[1,2];Wang, Zhe[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, Xining 810016, Peoples R China

会议论文集：IEEE International Conference on Multimedia and Expo (ICME)

会议日期：JUL 10-14, 2023

会议地点：Brisbane, AUSTRALIA

语种：英文

外文关键词：Sparse Subspace Clustering; Self-Representation; Manifold Learning; Projection Learning

摘要：Sparse subspace clustering algorithm cluster the data points located on the union of low-dimensional subspaces through the L-1 minimization program. However, the L-1-norm is not rotation invariant, and utilizing original data containing noise as the dictionary leads to poor performance. This paper proposes a method named robust structured sparse subspace clustering with neighborhood preserving projection (RSSSC). Firstly, RSSSC replaces the L-1 minimization program with a structured re-weighting sparse regularization term, effectively recovering the sparse representation. Secondly, RSSSC uses the extracted features as the dictionary in the self-representation problem to replace the original dataset containing noise and outliers, thus making the model more robust. By fully considering the low-dimensional manifold structure of samples in the original high-dimensional space, RSSSC preserves the neighborhood structure while learning the optimal projection. We verify the effectiveness of the proposed method through experiments on real-world image datasets.