Probabilistic low-rank matrix completion on finite alphabets

TL;DR

This paper studies the problem of reconstructing matrices with finite alphabet entries, providing theoretical bounds for estimators and proposing an efficient algorithm for high-dimensional cases.

Contribution

It introduces a probabilistic approach for low-rank matrix completion with finite alphabets, including theoretical analysis and a scalable algorithm.

Findings

Derived bounds for Kullback-Leibler divergence between true and estimated distributions.

Proposed an efficient lifted coordinate gradient descent algorithm.

Applicable to high-dimensional matrix completion problems.

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.