文献类型：期刊文献

英文题名：Factored Trace Lasso Based Linear Regression Methods: Optimizations and Applications

作者：Zhang, Hengmin[1]; Du, Wenli[1]; Liu, Xiaoqian[2]; Zhang, Bob[3]; Qian, Feng[1]

机构：[1] School of Information Science and Engineering, Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] Department of Computer Information and Cyber Security, Jiangsu Police Institute, Nanjing, 210031, China; [3] Department of Computer and Information Science, University of Macau, 999078, China

年份：2021

卷号：1397 CCIS

起止页码：121

外文期刊名：Communications in Computer and Information Science

收录：EI(收录号：20212210417956)

语种：英文

外文关键词：Regression analysis - Clustering algorithms - Numerical methods

摘要：Consider that matrix trace lasso regularized convex p -norm with p= 1, 2 regression methods usually have the higher computational complexity due to the singular value decomposition (SVD) of larger size matrix in big data and information processing. By factoring the matrix trace lasso into the squared sum of two Frobenius-norm, this work studies the solutions of both adaptive sparse representation (ASR) and correlation adaptive subspace segmentation (CASS), respectively. Meanwhile, the derived models involve multi-variable nonconvex functions with at least two equality constraints. To solve them efficiently, we devise the nonconvex alternating direction multiplier methods (NADMM) with convergence analysis satisfying the Karush-Kuhn-Tucher (KKT) conditions. Finally, numerical experiments to the subspace clustering can show the less timing consumptions than CASS and the nearby performance of our proposed method when compared with the existing segmentation methods like SSC, LRR, LSR and CASS. ? 2021, Springer Nature Singapore Pte Ltd.