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Rank Minimization - A Way to 2D Sparsity
澳门新葡8455最新网站:2010年12月03日 00:00 点击数:

报告人:林宙辰

报告地点:澳门新葡8455最新网站 5楼501室

报告澳门新葡8455最新网站:2010年12月08日星期三 下午 4:00 - 5:30

邀请人:

报告摘要:

Sparse representation has been a hot topic in signal processing and machine learning in recent years. In the past, people usually discussed 1D sparsity, i.e., the number of nonzeros in a vector. For 2D signals, we can actually also define a measure of sparsity, namely the rank of the data matrix. In this talk, I will give a brief history of sparse representation and then introduce some core problems and theories of rank minimization. Finally, I will show some applications of rank minimization that have resulted in improved performance in handling imperfect data that can have noise, outliers and missing values.

主讲人概况:

Dr. Zhouchen Lin is a Lead Researcher at Visual Computing Group, 微软 Research Asia. He received the Ph.D. degree in applied mathematics from Peking University in 2000. He is now a guest professor to Shanghai Jiaotong University, Beijing Jiaotong University and Southeast University. He is also a guest researcher to Institute of Computing Technology, Chinese Academy of Sciences. His research interests include computer vision, image processing, computer graphics, machine learning, pattern recognition, and numerical computation and optimization. He is a Senior member of the IEEE.

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