【廿周年院庆学术报告127】 · 【和山数学论坛第401期】

一、报告题目：Efficient Randomized Algorithms for Low-Rank Approximation of Large Tensors

二、报告人：喻高航 教授

三、时 间：2024年1月9日(周二) 上午 10:00-10:30

四、地 点：闻理园A3-217

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. Numerical experiments on synthetic and real-world tensor data demonstrate the competitive performance of the proposed algorithms.

报告人简介：喻高航，杭州电子科技大学“西湖学者”特聘教授、博士生导师，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, Journal of Mathematical Imaging and Vision, Journal of Scientific Computing，Knowledge-Based Systems, Expert Systems with Applications, IEEE Transactions on Computational Social Systems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 的期刊编委和国际学术期刊Statistics, Optimization and Information Computing执行主编(Coordinating Editor)。

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