报告人：喻高航 教授
报告题目：Large Scale Tensor Decomposition: Randomized method and Its Applications
报告时间：2025年11月26日（周三）上午11：00
报告地点：云龙校区6号楼304会议室
主办单位：数学与统计学院、数学研究院、科学技术研究院
报告人简介：
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，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SCI学术期刊Intelligent Automation & Soft Computing 的期刊编委；国际学术期刊Statistics, Optimization and Information Computing执行编委（Coordinating Editor）。
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 decomposition, with rigorous error-bound analysis. We also present some applications on tensor completion and parameter-efficient-fine-tuning (PEFT) for transfer learning.
