2015年6月23日，北京大学林宙辰教授应电子工程与信息科学系和多媒体计算与通信-教育部微软重点实验室的邀请，在科大西区科技实验楼西楼1213会议室做题为《 Subspace Clustering – Recent Advances》的高水平学术前沿讲座。该讲座中，林教授深入浅出地介绍了子空间聚类的研究背景和发展情况，讨论了其中的关键科学问题，着重介绍了其课题组近年来在子空间聚类方面取得的多个个代表性的研究成果，并与参会师生进行了广泛的交流和讨论。 

　　图1 报告人：林宙辰教授 (北京大学)

　　图2 林宙辰教授做题为《Subspace Clustering – Recent Advances》的报告。

　　报告摘要：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.

　　报告人简介：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 IEEE Trans. Pattern Analysis and Machine Intelligence and International J. Computer Vision, a Senior member of the IEEE, and an area chair of CVPR2014, ICCV2015, NIPS2015, and AAAI2016.