Multi-view learning
Dr. Guoqing Chao

Speaker Event details

Dr. Guoqing Chao
Postdoctoral Fellow
Medical school
Northwestern University, Chicago, U.S




18 June 2018, Monday

10.00am 11.00am

Meeting Room 4-4, Level 4
School of Information Systems
Singapore Management University
80 Stamford Road
Singapore 178902

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Multi-view learning is the problem of machine learning combing multiple feature sets or views of the data. Due to the extensively existing of multi-view data in real world, multi-view learning attracted much attention in the past decades. Both co-training and co-regularization style methods are two common categories of the multi-view learning methods. Consensus and complementarity are two significant principles that are followed in most of the existing successful multi-view learning methods. In this topic, I will introduce two multi-view learning methods I did, one proposed a new way to combine multi-view information, and the other one integrated consensus and complementarity principles into a unified framework to conduct multi-view learning.

About the speaker

Dr. Guoqing Chao obtained his Ph.D degree from the Computer Science and Technology Department of East China Normal University, Shanghai, China in 2015. From 12/2015 - 12/2017, he worked as a PostDoc fellow in Computer Science and Engineering department in the University of Connecticut, Storrs, CT, U.S. From 12/2017-present, he worked as a PostDoc fellow in Medical school of Northwestern University, Chicago, U.S. His research interest include machine learning, data mining, pattern recognition, bioinformatics and medical informatics.


This research is supported by the National Research Foundation, Prime Minister's Office, Singapore under its International Research Centres in Singapore Funding Initiative.

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