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arXiv · 1412.2632

Probabilistic low-rank matrix completion on finite alphabets

Jean Lafond·Olga Klopp·Eric Moulines·Jospeh Salmon
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Abstract

The task of reconstructing a matrix given a sample of observedentries is known as the matrix completion problem. It arises ina wide range of problems, including recommender systems, collaborativefiltering, dimensionality reduction, image processing, quantum physics or multi-class classificationto name a few. Most works have focused on recovering an unknown real-valued low-rankmatrix from randomly sub-sampling its entries.Here, we investigate the case where the observations take a finite number of values, corresponding for examples to ratings in recommender systems or labels in multi-class classification.We also consider a general sampling scheme (not necessarily uniform) over the matrix entries.The performance of a nuclear-norm penalized estimator is analyzed theoretically.More precisely, we derive bounds for the Kullback-Leibler divergence between the true and estimated distributions.In practice, we have also proposed an efficient algorithm based on lifted coordinate gradient descent in order to tacklepotentially high dimensional settings.

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Jean Lafond, Olga Klopp, Eric Moulines, Jospeh Salmon. 2014-12-08. Probabilistic low-rank matrix completion on finite alphabets. https://arxiv.org/abs/1412.2632

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