12/20に九州大学伊都キャンパス ウエスト2号館3F システム情報科学府第5+6講義室で開催されます，張超（ZHANG Chao）副教授 （北京大学 信息科学技術学院）による「Subspace Recovery Based Feature Learning」を共催いたします．
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[Title]
Subspace Recovery Based Feature Learning
[Abstract]
Subspace recovery based feature learning methods is a kind of feature
learning method which is proposed recently. This kinds of methods aim
at characterizing the relationship between data samples and then
learning more discriminative feature by recovering the inherent
subspace structure of data. Actually, this kinds of methods have been
successfully applied to machine learning and computer vision problems,
such as face recognition, object classification, background modeling,
and visual saliency. In this talk I will describe recent research in
our lab on subspace recovery based feature learning that attempts to
combines feature learning with classification, so that the regulated
classification error is minimized. In this way, the extracted features
are more discriminative for the recognition tasks.
[Biography]
Chao Zhang is an Associate Professor at the School of Electronics
Engineering and Computer Science of Peking University. He received the
Ph.D. degree in electrical engineering from Beijing Jiaotong
University, Beijing, China, in 1995. He was a Post-Doctoral Research
Fellow with the National Laboratory on Machine Perception, Peking
University, Beijing, from 1995 to 1997. He has been an associate
professor with the Key Laboratory of Machine Perception(MOE), School
of Electronics Engineering and Computer Science, Peking University
since 1997. His current research interests include image processing,
statistical pattern recognition, and visual recognition. He has
published more than 60 papers in refereed journals and conferences in
these areas, and received 6 national invention patents.