Probabilistic low-rank matrix completion with adaptive spectral regularization algorithms

Probabilistic low-rank matrix completion with adaptive spectral regularization algorithms

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...

Bibliografiset tiedot
Päätekijät:	Todeschini, A, Caron, F, Chavent, M
Aineistotyyppi:	Conference item
Julkaistu:	Neural information processing systems foundation 2013

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