报告题目：Practical Sketching Algorithms for Low-Rank Approximation of Large Tensors

报告人：喻高航 教授 (杭州电子科技大学)

时间： 6月2日（周五）下午4:00--5:00

地点： 理科楼A404                                                                                                

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.