Invited Speaker---Dr. Yonggang Lu, Professor
School of Information Science and Engineering, Lanzhou University, Lanzhou, China
Yonggang Lu is now working as a professor in the School of Information Science and Engineering, Lanzhou University, Lanzhou, China. He is a member of Chinese Computer Federation, IEEE and ACM. He received both the B.S. and M.S. Degrees in Physics from Lanzhou University, Lanzhou, China in 1996 and 1999 respectively. Later he received the M.S. and Ph.D. Degrees in Computer Science from New Mexico State University, Las Cruces, NM, USA in 2004 and 2007 respectively. He finished some of the Ph.D. work at Los Alamos National Lab, NM, USA. His main research interests include artificial Intelligence, machine learning, pattern recognition, image processing and bioinformatics. He has presided over a Chinese Nation Science Foundation project and other projects and has published over 50 research papers. He is a reviewer for many journals, including Neurocomputing, IEEE Access, IEEE Trans. on Neural Networks and Learning Systems, International Journal of Pattern Recognition and Artificial Intelligence, IEEE Trans. On Systems, Man, and Cybernetics, Journal of Computational Biology, IEEE & ACM Transactions on Computational Biology and Bioinformatics, Web Intelligence, Molecular Based Mathematical Biology, Computers and Electrical Engineering, and Advances in Mechanical Engineering.
Hypergraph Clustering by Generating Large Pure Hyperedges Using Greedy Neighborhood Search
Pairwise similarity between data points is usually computed in the traditional clustering methods. But in many cases, especially for high dimensional data in computer vision, it is required that more than two data points should be involved in representing the similarities. In this case, hypergraph clustering is an ideal tool for data analysis, where high order similarities on the data subsets, represented by hyperedges, can reflect the similarity among more than two data points. Hypergraph clustering usually includes hypergraph construction and hypergraph partition. Two important questions in hypergraph construction are how to generate the hyperedges and how many hyperedges should be used to represent the original data. Recently, Pulak Purkait et al. have proposed a method for generating the large pure hyperedges, which is proved to be more effective than the traditional methods for computer vision tasks. However, the method needs a specified number of hyperedges in advance, and uses random sampling to generate hyperedges, which may lead to suboptimal clustering results. Therefore, a novel sampling method called greedy neighborhood search is proposed in this work, which generates large pure hyperedges based on Shared Reverse k Nearest Neighbors (SRNN) and learns the number of hyperedges simultaneously. Experiments show the benefits of applying the proposed method on high dimensional data.