报告题目：Low-Rank Tensor Representation for High-Dimension Data Processing

报 告 人：丁猛 副教授（西南交通大学）

负 责 人：张枫

报告时间：2025年12月22日（星期一）下午17:00-18:00

报告地点：数学大楼报告厅四（912）

参加人员：教师、研究生、本科生

报告摘要：Tensor analysis has received widespread attention in high-dimensional data learning. Unfortunately, the tensor data are often degraded by noise or insufficient for subspace representation. In this talk, we first study a generalized transformed tensor low-rank representation model for simultaneously recovering and clustering the corrupted tensor data and prove the recoverability with a high probability guarantee. Besides, we use hidden tensor data to address the problem of insufficient observed samples. We employ both observed samples and hidden tensor data under low-rank constraints so that a new bilateral tensor low-rank representation in subspace clustering is formulated. Furthermore, we introduce the bilateral low-rank representation to multi-view subspace clustering. Experiments on various datasets showcase the outstanding performance.

报告人简介：丁猛，西南交通大学数学学院副教授，博士生导师。主要研究方向是高维数据处理的张量建模与算法研究，目前已在数学权威期刊SIIMS, JSC, IPI，IEEE系列权威期刊JSTSP, TKDE, TNNLS等发表学术论文，其中2篇入选ESI高被引论文。主持国家自然科学基金青年基金项目一项和四川省科技厅项目一项，参与多个国家自然科学基金项目、华为公司合作项目的研究，相关成果荣获四川省数学会第二届应用数学奖一等奖。