An Asymmetric Kernel for Compressed Classification

Prof. Kar-Ann Toh, Yonsei University, Seoul, Korea

Organized by IEEE Signal Processing Singapore Chapter & IEEE Circuits and Systems Singapore Chapter & Centre for Information Sciences and Systems (ICSS), School of EEE, NTU & Singapore University of Social Sciences (SUSS)

Date : 07 January 2021 (Thursday)
Time : 2.30 PM
Venue : Online Seminar (Zoom)

To register, please click the link below. You will receive the Zoom link and password after registration. https://suss.zoom.us/meeting/register/tJAudeyopjoqGdNr_6b4h_6Q08TQj57RhRl4

Abstract

According to Herbert A. Simon, “the more relevant patterns at your disposal, the better your decisions will be.” In this talk, we introduce an asymmetric kernel for compressed learning representation. The kernel can be utilized for stretchable learning where feature compression and target fitting can be performed at the same time. The learning is subsequently extended to classifier learning where an error counting objective is desired. Some numerical examples on benchmark datasets will be given to demonstrate the effectiveness of the learning method.

Speaker Biography

Kar-Ann Toh is a Professor in the School of Electrical and Electronic Engineering at Yonsei University, South Korea. He received the PhD degree from Nanyang Technological University (NTU), Singapore in 1999. He worked for two years in the aerospace industry prior to his post-doctoral appointments at research centers in NTU from 1998 to 2002. He was affiliated with the Institute for Infocomm Research in Singapore from 2002 to 2005 prior to his current appointment in Korea. He was a Visiting Professor at National University of Singapore in the year 2020 during his sabbatical leave. His research interests include biometrics, pattern classification, and machine learning. He has served/is serving as an Associate Editor of several international journals including IEEE Transactions on Biometrics, Behavior and Identity Science, IEEE Transactions on Information Forensics and Security, Journal of Franklin Institute, Pattern Recognition Letters, and IET Biometrics.

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