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...
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Neural information processing systems foundation
2013
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