报告题目：Low-Rank and Sparse Enhanced Tucker Decomposition for Tensor Completion
报 告 人：何洪津（杭州电子科技大学 副教授）
报告时间：2020年10月21日 10:00开始
报告地点：阳明学院303会议室
报告摘要：Tensor completion refers to the task of estimating the missing data from an incomplete measurement or observation, which is a core problem frequently arising from the areas of big data analysis, computer vision, and network engineering. Due to the multidimensional nature of high-order tensors, the matrix approaches, e.g., matrix factorization and direct matricization of tensors, are often not ideal for tensor completion and recovery. Exploiting the potential periodicity and inherent correlation properties appeared in real-world tensor data, in this talk, we shall incorporate the low-rank and sparse regularization technique to enhance Tucker decomposition for tensor completion. A series of computational experiments on real-world datasets, including color images and face recognition, show that our approach performs better than many existing state-of-the-art matricization and tensorization approaches in terms of achieving higher recovery accuracy. (Joint work with C. Pan, C. Ling, L.Q. Qi, and Y. Xu)
报告人简介：男，副教授，硕士生导师，2012年6月博士毕业于南京师范大学计算数学专业。主要研究方向为数值优化及其在图像处理、机器学习等领域中的应用。发表学术论文40余篇，研究成果发表在Numerische Mathematik, Inverse Problems, Journal of Scientific Computing, Science China Mathematics等国际权威期刊。主持完成国家面上、青年基金和省基金一般科研项目4项，参与国家面上、省重大、重点项目5项。2017年10月入选浙江省高校中青年学科带头人。
