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Probabilistic low-rank matrix completion with adaptive spectral regularization algorithms

Abstract:

We propose a novel class of algorithms for low rank matrix completion. Our approach builds on novel penalty functions on the singular values of the low rank matrix. By exploiting a mixture model representation of this penalty, we show that a suitably chosen set of latent variables enables to derive an Expectation-Maximization algorithm to obtain a Maximum A Posteriori estimate of the completed low rank matrix. The resulting algorithm is an iterative soft-thresholded algorithm which iterativel...

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Publisher:
Neural information processing systems foundation
Host title:
Advances in Neural Information Processing Systems
Publication date:
2013-01-01
ISSN:
1049-5258
Source identifiers:
463446
Pubs id:
pubs:463446
UUID:
uuid:38a98cc2-e501-46b0-b013-bad5e7eb8c02
Local pid:
pubs:463446
Deposit date:
2014-11-11

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