摘要： Robust estimation and sparse regularization are involved in many scientific and engineering problems. The former is required when there are outliers or impulsive disturbances in the observed data, while the latter is a standard technique for handling sparse recovery, namely, finding a solution with the minimum number of nonzero entries in a certain domain. In this talk, the connection between robust estimation and sparse regularization will be revealed, and a new framework for a robust loss function will be presented to address these two topics. Application examples of robust factorization based and rank minimization based matrix recovery are included to demonstrate the effectiveness of the proposed framework.
主讲人： Hing Cheung So, Professor, City University of Hong Kong, China
Hing Cheung So was born in Hong Kong. He received the B.Eng. degree from the City University of Hong Kong and the Ph.D. degree from The Chinese University of Hong Kong, both in electronic engineering, in 1990 and 1995, respectively. From 1990 to 1991, he was an Electronic Engineer with the Research and Development Division, Everex Systems Engineering Ltd., Hong Kong. During 1995–1996, he was a Postdoctoral Fellow with The Chinese University of Hong Kong. From 1996 to 1999, he was a Research Assistant Professor with the Department of Electronic Engineering, City University of Hong Kong, where he is currently a Professor. His research interests include detection and estimation, fast and adaptive algorithms, multidimensional harmonic retrieval, robust signal processing, source localization, and sparse approximation. He has been on the editorial boards of IEEE Signal Processing Magazine, IEEE Transactions on Signal Processing, Signal Processing, and Digital Signal Processing.
主要与会者：抖音黑料
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