好文作者面授招–20151202

【15-36期VALSE Webinar活动】

报告嘉宾:杨颖振(UIUC ECE Dept.)
报告时间:
2015年12月02日(星期三)晚20:30(北京时间)
报告题目: 
Learning with L0-Graph: L0-Induced Sparse Subspace Clustering [Slides]
主持人:
禹之鼎(CMU ECE Dept.)
报告摘要:
Sparse subspace clustering methods, such as Sparse Subspace Clustering (SSC) and L1-graph, are effective in partitioning the data that lie in a union of subspaces. Most of those methods use L1-norm or L2 -norm with thresholding to impose the sparsity of the constructed sparse similarity graph, and certain assumptions, e.g. independence or disjointness, on the subspaces are required to obtain the subspace-sparse representation, which is the key to their success. Such assumptions are not guaranteed to hold in practice and they limit the application of sparse subspace clustering on subspaces with general location. In this paper, we propose a new sparse subspace clustering method named L0-graph. In contrast to the required assumptions on subspaces for most existing sparse subspace clustering methods, it is proved that subspace- sparse representation can be obtained by L0-graph for arbitrary distinct underlying subspaces almost surely under the mild i.i.d. assumption on the data generation. We develop a proximal method to obtain the sub- optimal solution to the optimization problem of L0-graph with proved guarantee of convergence. Moreover, we propose a regularized L0-graph that encourages nearby data to have similar neighbors so that the similarity graph is more aligned within each cluster and the graph connectivity issue is alleviated. Extensive experimental results on various data sets demonstrate the superiority of L0-graph compared to other competing clustering methods, as well as the effectiveness of regularized L0-graph.
参考文献:
[1] Yingzhen Yang, Jiashi Feng, Jianchao Yang, and Thomas S. Huang. Learning with L0-Graph: L0-Induced Sparse Subspace Clustering. (Arxiv:1511.04601, 2015)
报告人简介:
Yingzhen Yang is a Ph.D. candidate with the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, under the supervision of Professor Thomas S. Huang. His current research interests include machine learning with focus on statistical learning theory, large-scale probabilistic graphical models, sparse coding, manifold learning and nonparametric methods; computer vision with focus on image classification using deep learning methods and image/video enhancement. He is the recipient of “Lu Zeng Yong” CAD&CG High-Tech Award in China in 2009 and Carnegie Institute of Technology Dean’s Tuition Fellowship in 2010.

报告嘉宾:孙欢(UCSB CS Dept., Joining OSU CSE Dept. as Faculty)
报告时间:2015年12月02日(星期三)晚21:30(北京时间)
报告题目:Intelligent and Collaborative Query Resolution [Slides]
主持人:禹之鼎(CMU ECE Dept.)
报告摘要:

The paradigm of information search is undergoing a significant transformation due to the rise of mobile devices. Unlike traditional search engines retrieving numerous webpages, techniques that can precisely and directly answer user questions are becoming more desired. In this talk, I will discuss two strategies: (1) Machine intelligent query resolution, where I will present two novel frameworks: (i) Schema-less knowledge graph querying. This framework directly searches knowledge bases to answer user queries. It successfully deals with the challenge that answers to user queries could not be simply retrieved by exact keyword and graph matching, due to different information representations. (ii) Combining knowledge bases with the Web. We recognized that knowledge bases are usually far from complete and information required to answer questions may not always exist in knowledge bases. This framework mines answers directly from large-scale web resources, and meanwhile employs knowledge bases as a significant auxiliary to boost question answering performance. (2) Human collaborative query resolution. We made the first attempt to quantitatively analyze expert routing behaviors, i.e., how an expert decides where to transfer a question when she could not solve it. A computational routing model was then developed to optimize team formation and team communication for more efficient problem solving. I will conclude by discussing future directions, including leveraging both machine and human intelligence for better question answering and decision making in healthcare and business intelligence.
参考文献:
[1] Huan Sun, Hao Ma, Wen-tau Yih, Chen-Tse Tsai, Jingjing Liu, and Ming-Wei Chang. Open Domain Question Answering via Semantic Enrichment. (WWW 2015)
[2] Huan Sun, Mudhakar Srivatsa, Shulong Tan, Yang Li, Lance M. Kaplan, Shu Tao, and Xifeng Yan. Analyzing Expert Behaviors in Collaborative Networks. (KDD 2014)
[3] Shengqi Yang, Yinghui Wu, Huan Sun, and Xifeng Yan. Schemaless and Structureless Graph Querying. (PVLDB 7(7), 2014)
报告人简介:
Huan Sun received her Ph.D. in the Department of Computer Science at the University of California, Santa Barbara. She will join the Department of Computer Science and Engineering at the Ohio State University in Fall 2016. Her research interests lie in data mining and machine learning, with emphasis on text mining, network analysis and human behavior understanding. Particularly, she has been investigating how to model and combine machine and human intelligence for question answering and knowledge discovery. Prior to UCSB, Huan received her B.S. in EE from the University of Science and Technology of China. She received the UC Regents’ Special Fellowship and the CS Ph.D. Progress Award in 2014. She did summer internships at Microsoft Research and IBM T.J. Watson Research Center.

(Visited 716 times, 1 visits today)