Fixed Point Iteration Algorithms for Low-rank Matrix Completion
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Date
2015-05-20
Authors
Huang, Xingliang
Advisor
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
A lot of applications can be formulated as matrix completion problems. In order to
address such problems, a common assumption is that the underlying matrix is (approximately)
low-rank. Under certain conditions, the recovery of low-rank matrix can be done
via nuclear norm minimization, a convex program.
Scalable and fast algorithms are essential as the practical matrix completion tasks always
occur on a large scale. Here we study two algorithms and generalize the uni ed
framework of xed point iteration algorithm. We derive the convergence results and propose
a new algorithm based on the insights. Compared with the baseline algorithms, our
proposed method is signi cantly more e cient without loss of precision and acceleration
potentiality.
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