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NCTS Course on Low Rank Matrix Completion and its Application in Image Processing, Machine Learning, etc.
(Mon.) 11:00-12:00, 14:00-17:00, March 27, 2017
M107, Hong-Jing Hall

Jian-Feng Cai (The Hong Kong University of Science and Technology)

I-Liang Chern (Institute of Applied Mathematical Sciences, National Taiwan University)

In this short course, we consider the problem of how to recover a low rank matrix from its limited information, which arises from
numerous practical applications in machine learning, imaging, signal processing, computer vision, etc. Typically, the low rank
matrix to be recovered is of large scale and the number of linear measurements is small. We will start with some examples that
can be formulated to the low-rank matrix reconstruction problem. Then we will introduce both convex and non-convex algorithms,
as well as the theory on the guarantee of their convergence to the correct low-rank matrix.

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 (C) 2017 National Center for Theoretical Sciences