Yazhou Ren is currently an Associate Professor with the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China. He received the B.Sc. degree in information and computation science and the Ph.D. degree in computer science from the South China University of Technology, Guangzhou, China, in 2009 and 2014, respectively. He visited the Data Mining Laboratory, George Mason University, USA, from 2012 to 2014. His current research interests include Artificial Intelligence, Machine Learning, and Intelligent Healthcare. He has published more than 70 papers in refereed journals and conferences, such as AAAI, IJCAI, CVPR, ICCV, NeurIPS, ACM MM, SIGIR, ICDM, ECCV, TKDE, TGRS, NN, and KBS.
Self-Supervised Feature Learning for Deep Multi-View Clustering
Cluster analysis plays an indispensable role in machine learning and data mining. In practical clustering tasks, the input data usually have multiple views. Recently, the application of deep learning in multi-view clustering has attracted people’s increasing attention. Deep multi-view clustering algorithms focus on solving the clustering problems with different forms of input data, and making use of the complementary information among multiple views to improve clustering performance. In this talk, I will introduce our two recent works (Deep Embedded Multi-View Clustering with Collaborative Training, DEMVC; and Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering, SDMVC) and address some hot research directions of deep multi-view clustering. Hope that through our sharing, this research area will produce more surprising discoveries.