文献类型：期刊文献

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] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [3] Qinghai University, Xining, 810016, China

年份：2023

卷号：2023-July

起止页码：1781

外文期刊名：Proceedings - IEEE International Conference on Multimedia and Expo

收录：EI(收录号：20233814738651)

语种：英文

外文关键词：Structural optimization

摘要：Sparse subspace clustering algorithm cluster the data points located on the union of low-dimensional subspaces through the L1 minimization program. However, the L1-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 L1 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. ? 2023 IEEE.