Andrew TeohSchool of Electrical and Electronic Engineering College of Engineering, Yonsei University, Seoul,South Korea
Andrew Beng Jin Teoh obtained his BEng
(Electronic) in 1999 and Ph.D degree in 2003 from National University of
Malaysia. He affiliated with Multimedia University Malaysia from 2003 to 2007
as a senior lecturer and an associate dean in R&D of Faculty information
Science and Technology. He is currently an associate professor in Electrical
and Electronic Department, College Engineering of Yonsei University, South
Korea. His research, for which he has received funding, focuses Biometric
Security, specific in biometric template protection and bio-crypto key computation.
His current research interests are Pattern Recognition, Machine Learning and
Information Security. He has published more than 200 international refereed
journal, conference articles, and several book chapters. He is also a regular
speaker at conferences, academic institutions, and corporations. He has been a
reviewer for more than 30 journals and conferences. He has served conference
committees worldwide.
Talk : Dimension reduction
techniques in object recognition tasks
Dimension reduction (DR) is one of the core
components in pattern recognition and machine learning. In general, the
motivation of DR is to: reduce the dimensionality of feature space, speed up
and reduce the cost of a learning algorithm, improve the predictive accuracy of
a classification algorithm, and to improve the visualization and the
comprehensibility of the induced concepts. In this tutorial, we shall: (1)
focus on linear and non-linear feature extraction techniques. (2) see how
feature extraction can be perceived as a constraint or unconstraint
optimization problem. (3) investigate their roles in information extraction,
low-dimension structure preservation and object recognition tasks. |
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