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.