報告人：喻高航 教授
報告題目：Large Scale Tensor Decomposition: Randomized method and Its Applications
報告時間：2025年11月26日（周三）上午11：00
報告地點：云龍校區6號樓304會議室
主辦單位：數學與統計學院、數學研究院、科學技術研究院
報告人簡介：
喻高航，浙江科技大學教授、博導，主要從事張量數據分析、大規模優化計算及其在機器學習、圖像處理與醫學影像中的應用研究。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等國際期刊上發表50余篇SCI論文,先后主持5項國家自然科學基金、1項教育部新世紀優秀人才支持計劃項目和1項浙江省自然科學基金重大項目，有多篇論文入選ESI高被引榜單。現任國際SCI學術期刊Intelligent Automation & Soft Computing 的期刊編委；國際學術期刊Statistics, Optimization and Information Computing執行編委（Coordinating Editor）。
報告摘要：
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.