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Low rank matrix completion by alternating steepest descent methods

Abstract:

Matrix completion involves recovering a matrix from a subset of its entries by utilizing interdependency between the entries, typically through low rank structure. Despite matrix completion requiring the global solution of a non-convex objective, there are many computationally efficient algorithms which are effective for a broad class of matrices. In this paper, we introduce an alternating steepest descent algorithm (ASD) and a scaled variant, ScaledASD, for the fixed-rank matrix completion p...

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Publication status:
In Press
Peer review status:
Peer Reviewed

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Publisher copy:
10.1016/j.acha.2015.08.003

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Institution:
University of Oxford
Department:
Oxford, MPLS, Mathematical Inst
Journal:
Applied and Computational Harmonic Analysis
Publication date:
2015-08-05
DOI:
ISSN:
1063-5203
URN:
uuid:36aad9c2-9aed-4f89-9436-f7e19738b7bf
Source identifiers:
540694
Local pid:
pubs:540694

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