报告人：喻高航
报告地点：人民大街校区数学与统计学院二楼会议室
报告时间：2026年08月09日（星期日）09:30-10:30
邀请人：刘俊
报告摘要：
Low-rank approximation of tensors has been widely used in high-dimensional data analysis. It usually involves singular value decomposition (SVD) of large-scale matrices with high computational complexity. Sketching is an effective data compression and dimensionality reduction technique applied to the low-rank approximation of large matrices. This talk presents some efficient randomized algorithms for low-rank tensor approximation based on T-product, Tucker and Tensor Train decomposition, with rigorous error-bound analysis and some applications.
主讲人简介：
喻高航，浙江科技大学教授、博导，主要从事张量数据分析、大规模优化计算及其在机器学习、图像处理与医学影像中的应用研究。先后在SIAM Journal on Imaging Sciences, IEEE Transactions on Computational Social Systems，Pattern Recognition, Expert Systems with Applications，Knowledge-Based Systems，Journal of Scientific Computing，Applied Mathematical Modelling,Inverse Problems, Journal of Optimization Theory and Applications, Optimization Methods and Software等国际期刊上发表50余篇SCI论文,先后主持5项国家自然科学基金、1项教育部新世纪优秀人才支持计划项目和1项浙江省自然科学基金重大项目，有多篇论文入选ESI高被引榜单。现任国际学术期刊Statistics, Optimization and Information Computing执行编委。
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