电子科技大学李长升研究员学术报告会

来源:信息科学与技术学院  作者:袁召全  日期:2019-11-20  点击数:453

报告题目(Title):  Active Sample and Feature Learning: A Connection Between Them


报告专家(Speaker):  李长升 研究员
报告地点(Venue):  西南交通大学犀浦校区9号楼X9322会议室
报告时间(Time):  2019-11-21 周四 10:30-12:00
主持人(Chair):  袁召全

内容提要(Outline of the talk):
This talk will present an unsupervised learning approach for simultaneous sample and feature selection, which is in contrast to existing works which mainly tackle these two problems separately. In fact the two tasks are often interleaved with each other: noisy and high-dimensional features will bring adverse effect on sample selection, while ‘good’ samples will be beneficial to feature selection. Specifically, we propose a framework to jointly conduct active learning and feature selection based on the CUR matrix decomposition. From the data reconstruction perspective, both the selected samples and features can best approximate the original dataset respectively, such that the selected samples characterized by the features are highly representative. In particular, our method runs in one-shot without the procedure of iterative sample selection for progressive labeling. Thus, our model is especially suitable when there are few labeled samples or even in the absence of supervision, which is a particular challenge for existing methods. As the joint learning problem is NP-hard, the proposed formulation involves a convex but non-smooth optimization problem. We solve it efficiently by an iterative algorithm, and prove its global convergence. Experimental results on publicly available datasets corroborate the efficacy of our method compared with the state-of-the-art.

报告人简介(Short Biography of the speaker):
Changsheng Li is currently a Professor from the University of Electronic Science and Technology of China (UESTC) since 2017. He is also a vice dean leading to an team to advance artificial intelligent technologies, and apply them to solving practical problems. He received his B.E. degree from the University of Electronic Science and Technology of China (UESTC) in 2008, and his Ph.D. degree in pattern recognition and intelligent system from the Institute of Automation, Chinese Academy of Sciences in 2013. He also studied as a Research Assistant in The Hong Kong Polytechnic University in 2009-2010. Prior to joining in UESTC, He once worked as Research Scientist in IBM Research and iDST, Alibaba Group. He received the IBM Research Accomplishment Award in 2015. His research interests include machine learning, pattern recognition and computer vision. Dr. Li has more than 40 refereed publications in international journals and conferences, including T-PAMI, T-NNLS, T-IP, T-C, PR, CVPR, AAAI, IJCAI, CIKM, MM, ICMR,etc.