摘要：

Nowadays we are in the big data era, where the data is usually high dimensional. How to process high dimensional data effectively is a critical issue. Fortunately, we observe that data usually distribute near low dimensional manifolds. Mixture of subspaces is a simple yet effective model to represent high dimensional data, where the membership of the data points to the subspaces might be unknown. Therefore, there is a need to simultaneously cluster the data into multiple subspaces and find a low-dimensional subspace fitting each group of data points. This problem, known as subspace clustering, has found numerous applications. In this talk, I will present my recent work on this research problem.

简历：

ZHOUCHEN LIN received the Ph.D. degree in applied mathematics from Peking University in 2000. He is currently a Professor at Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Peking University. He is also a Chair Professor at Northeast Normal University and a guest professor at Beijing Jiaotong University. Before March 2012, he was a Lead Researcher at Visual Computing Group, Microsoft Research Asia. He was a guest professor at Shanghai Jiaotong University and Southeast University, and a guest researcher at Institute of Computing Technology, Chinese Academy of Sciences. His research interests include computer vision, image processing, computer graphics, machine learning, pattern recognition, and numerical computation and optimization. He is an associate editor of International J. Computer Vision and a Senior member of the IEEE. He will serve CVPR2014 as an area chair.