报告题目： Rank Minimization: A Way to 2D Sparsity
报告人：林宙辰博士 微软亚洲研究院视觉计算组主管研究员(Lead Researcher)
报告摘要：Sparse representation has been a hot topic in signal processing and machine learning in recent years. In the past, people usually discussed 1D sparsity, i.e., the number of nonzeros in a vector. For 2D signals, we can actually also define a measure of sparsity, namely the rank of the data matrix. In this talk, I will give a brief history of sparse representation and then introduce some core problems and theories of rank minimization. Finally, I will show some applications of rank minimization that have resulted in improved performance in handling imperfect data that can have noise, outliers and missing values.
报告人简介：Dr. Zhouchen Lin is a Lead Researcher at Visual Computing Group, Microsoft Research Asia. He received the Ph.D. degree in applied mathematics from PekingUniversity in 2000. He is now a guest professor to ShanghaiJiaotongUniversity, BeijingJiaotongUniversity and SoutheastUniversity. He is also a guest researcher to Institute of Computing Technology, ChineseAcademy of Sciences. His research interests include computer vision, image processing, computer graphics, machine learning, pattern recognition, and numerical computation and optimization. He is a senior member of the IEEE.