Conference item
Probabilistic low-rank matrix completion with adaptive spectral regularization algorithms
- Abstract:
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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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Bibliographic Details
- Publisher:
- Neural information processing systems foundation
- Host title:
- Advances in Neural Information Processing Systems
- Publication date:
- 2013-01-01
- ISSN:
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1049-5258
- Source identifiers:
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463446
Item Description
- Pubs id:
-
pubs:463446
- UUID:
-
uuid:38a98cc2-e501-46b0-b013-bad5e7eb8c02
- Local pid:
- pubs:463446
- Deposit date:
- 2014-11-11
Terms of use
- Copyright date:
- 2013
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