9月9日学术报告2(香港理工大学张磊：Weighted Nu

9月9日学术报告2(香港理工大学张磊：Weighted Nu

类别：学术报告发布人：admin 浏览次数：次发布时间：2015-09-24 15:07

报告题目：Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision

报告日期及时间：2015年9月9日下午14：30
报告地点：计算机学院B403
报告人：张磊教授
报告人国籍：中国
报告人单位：香港理工大学

报告人简介：
Lei Zhang (M’04, SM’14) received the B.Sc. degree in 1995 from Shenyang Institute of Aeronautical Engineering, Shenyang,
P.R. China, the M.Sc. and Ph.D degrees in Control Theory and Engineering from Northwestern Polytechnical University, Xi’an,
P.R. China, respectively in 1998 and 2001. From 2001 to 2002, he was a research associate in the Dept. of Computing,
The Hong Kong Polytechnic University. From Jan. 2003 to Jan. 2006 he worked as a Postdoctoral Fellow in the Dept. of Electrical
and Computer Engineering, McMaster University, Canada. In 2006, he joined the Dept. of Computing, The Hong Kong Polytechnic
University, as an Assistant Professor. Since July 2015, he has been a Full Professor in the same department. His research interests
include Computer Vision, Pattern Recognition, Image and Video Processing, and Biometrics, etc. Dr. Zhang has published more
than 200 papers in those areas. By 2015, his publications have been cited more than 14,000 times in literature. Dr. Zhang is
currently an Associate Editor of IEEE Trans. on Image Processing, IEEE Trans. on CSVT and Image and Vision Computing. He
was awarded the 2012-13 Faculty Award in Research and Scholarly Activities. More information can be found in his homepage.
http://www4.comp.polyu.edu.hk/~cslzhang/.

报告摘要：As a convex relaxation of the rank minimization model, the nuclear norm minimization (NNM) problem has been attracting
significant research interest in recent years. The standard NNM regularizes each singular value equally, composing an easily
calculated convex norm. However, this restricts its capability and flexibility in dealing with many practical problems, where the
singular values have clear physical meanings and should be treated differently. We study the weighted nuclear norm minimization
(WNNM) problem, which adaptively assigns weights on different singular values. As the key step of solving general WNNM models,
the theoretical properties of the weighted nuclear norm proximal (WNNP) operator are investigated. We show that WNNP is
equivalent to a standard quadratic programming problem with linear constrains, which facilitates solving the original problem with
off-the-shelf convex optimization solvers. In particular, when the weights are sorted in a non-descending order, its optimal solution can be easily obtained in closed-form. Multiple extensions of WNNM, including robust PCA and matrix completion, can be readily constructed under the augmented Lagrange multiplier paradigm. The proposed WNNM methods achieve state-of-the-art performance in typical low level vision tasks, including image denoising, background subtraction and image inpainting.

邀请人：荆晓远教授

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