报 告 人：巩志国 澳门大学科技公司电脑及资讯科学系主任
报告时间：4月 30日（周一）9: 00～10: 30
报告地点：779cn太阳集团延长校区行健楼734室
邀 请 人：骆祥峰 副研究员
报告摘要：
Deducing trip related information from web-scale datasets has received very large amounts of attention recently. Identifying points of interest (POIs) in geo-tagged photos is one of these problems. The problem can be viewed as a standard clustering problem of partitioning two dimensional objects. In this work, we study spectral clustering which is the first attempt for the POIs identification. However, there is no unified approach to assign the clustering parameters; especially the features of POIs are immensely varying in different metropolitans and locations. To address this, we are intent to study a self-tuning technique which can properly assign the parameters for the clustering needed.
Besides geographical information, web photos inherently store rich information. These information are mutually influenced each others and should be taken into trip related mining tasks. To address this, we study reinforcement which constructs the relationship over multiple sources by iterative learning. At last, we thoroughly demonstrate our findings by web scale datasets collected from Flickr.
Brief Biography:
Zhiguo Gong Received the B.S. degree in mathematics from Hebei Normal University, Shijiazhuang, China, in 1983, the M.S. degree in mathematics from Peking University, Beijing, China, in 1988, and the Ph.D. degree in Computer Science, Institute of Mathematics, Chinese Academy of Sciences, Beijing, in 1998. He is currently an Associate Professor and the Head of computer science with the Department of Computer and Information Science, University of Macau, Macau, China. His research interests include databases, Web information retrieval, and Web mining.
