Practical Sketching Algorithms for Low-Rank Approximation of Large Tensors

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 two practical randomized algorithms for low-rank tensor approximation based on two-sided sketching and power scheme, with a 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, International Journal of Robust and Nonlinear Control，IEEE Signal Processing Letters，Journal of Mathematical Imaging and Vision等国际期刊上发表40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）。