新加坡国立大学电子与计算机工程系颜水成博士应邀来我系访问并作学术报告，有关信息如下：
时间：1月12日（周三）下午2:30
地点：南一楼东头三楼 电信系会议室
Title: Graph Construction and Mining Towards Contextualization and Scalability
Abstract:
This talk covers two aspects of graph. First, graph construction, a fundamental problem for many pattern recognition tasks, is restudied beyond traditional k-NN graph and epsilon-ball graph. Sparse coding is introduced for the construction of the so-called L1-graph to characterize more informative and robust one-to-many relations. The positive semidefinite property of the weight matrix of the enhanced collective L1-graph construction is theoretically proved based on low-rank objective. Several successful applications of L1-graph are introduced from both computer vision and multimedia areas. Then, by taking dense subgraph detection as an example, we introduce how to mine from large graph for certain tasks. More specifically, we define graph modes, which are the local maxima of graph density functions, to represent such dense subgraphs. We propose the graph shift procedure, which starts from every vertex, iteratively shifts the local small subgraph towards the nearest graph mode along a certain trajectory. Both theoretic analyses and experiments show that graph shift procedure is very efficient (only working on quite small subgraph for each step) and robust, especially when there exists large amount of noises and outliers.
Short Bio:
Dr. Yan Shuicheng is currently an Assistant Professor in the Department of Electrical and Computer Engineering at National University of Singapore, and the founding lead of the Learning and Vision Research Group (http://www.lv-nus.org). Dr. Yan's research areas include computer vision, multimedia and machine learning, and he has authored or co-authored about 190 technical papers over a wide range of research topics. He is an associate editor of IEEE Transactions on Circuits and Systems for Video Technology, and has been serving as the guest editor of the special issues for TMM and CVIU. He received the Best Paper Awards from ACM MM’10, ICME’10,
ICIMCS'09 and PREMIA 2008 Best Student Paper Award, the winner prize of the classification task in PASCAL VOC2010, and the honorable mention prize of the detection task in PASCAL VOC2010.
