学术报告
题目：Non-convex multi-view subspace clustering via simultaneously learning the representation tensor and affinity matrix
报告人：黎稳 教授单位：华南师范大学数学科学学院
时 间：2022年12月5日（周一）下午 16：00-17：00
#腾讯会议：422-428-336
报告人简介：黎稳，华南师范大学二级教授。现任中国数学会理事，广东省数学学会副理事长。曾任中国计算数学会理事、广东省工业与应用数学学会副理事长、广东省计算数学学会副理事长。研究方向：数值代数、张量计算及其应用。主持五项国家自然科学基金面上项目，作为核心成员参与广东省与国家自然科学基金集成项目一项。在学术刊物 Numer Math、SIAM J Optim、SIAM J Matrix Anal Appl、SIAM J Imaging Sci、Inverse Problems、IEEE Trans Signal Proc.、J Sci Comput、Comput Optim Appl和Pattern Recognition等发表学术论文多篇。研究成果分别获2011年和2020年广东省科学技术奖二等奖（排名第一）。
报告摘要： In this talk, we introduce a new non-convex multi-view subspace clustering model via tensor minimax concave penalty (MCP) approximation associated with rank minimization, which can simultaneously construct the low-rank representation tensor and affinity matrix in a unified framework. Specifically, the non-convex MCP approximation rank function is adopted to as a tighter tensor rank approximation to discriminate the dimension of features so that better accuracy can be achieved. In addition, we also address the local structure by including both hyper-Laplacian regularization and auto-weighting scheme into the objective function to promote the clustering performance. A corresponding iterative algorithm is then developed to solve the proposed model and the constructed iterative sequence generated by the proposed algorithm is shown to converge to the desirable KKT critical point. Extensive experiments on benchmark datasets have demonstrate the highly desirable effectiveness of the proposed model.