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A Riemannian rank-adaptive method for low-rank matrix completion
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Reporter:
Dr. Bin Gao, University of Munster
Inviter:
Aihui Zhou, Professor
Subject:
A Riemannian rank-adaptive method for low-rank matrix completion
Time and place:
14:30-15:30 February 24(Thursday)
Abstract:

In this talk, we consider the low-rank matrix completion problem which has been extensively studied in recent years. This problem can be solved by Riemannian optimization on a fixed-rank manifold. However, a drawback of the known approaches is that the rank parameter has to be fixed a priori. Instead, we consider the optimization problem on the set of bounded-rank matrices. We propose a Riemannian rank-adaptive method, which consists of fixed-rank optimization, rank increase step and rank reduction step. We explore its performance applied to the low-rank matrix completion problem. Numerical experiments on synthetic and real-world datasets illustrate that the proposed rank-adaptive method compares favorably with state-of-the-art algorithms. 

This is a joint work with P.-A. Absil.