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17

JMLR
2012

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Primal-Dual methods for sparse constrained matrix completion

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Primal-Dual methods for sparse constrained matrix completion

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We develop scalable algorithms for regular and non-negative matrix completion. In particular, we base the methods on trace-norm regularization that induces a low rank predicted matrix. The regularization problem is solved via a constraint generation method that explicitly maintains a sparse dual and the corresponding low rank primal solution. We provide a new dual block coordinate descent algorithm for solving the dual problem with a few spectral constraints. Empirical results illustrate the eﬀectiveness of our method in comparison to recently proposed alternatives.

Yu Xin, Tommi Jaakkola

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Constraint Generation | JMLR 2012 | Primal Solution | Programming Languages | Spectral Constraints |

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Added	27 Sep 2012
Updated	27 Sep 2012
Type	Journal
Year	2012
Where	JMLR
Authors	Yu Xin, Tommi Jaakkola

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