The task of estimating a matrix given a sample of observed
entries is known as the matrix completion problem. Most works on matrix
completion have focused on recovering an unknown real-valued low-rank
matrix from a random sample of its entries. Here, we investigate the case
of highly quantized observations when the measurements can take only a
small number of values. These quantized outputs are generated according to
a probability distribution parametrized by the unknown matrix of interest.
This model corresponds, for example, to ratings in recommender systems
or labels in multi-class classification. We consider a general, non-uniform,
sampling scheme and give theoretical guarantees on the performance of a
constrained, nuclear norm penalized maximum likelihood estimator. One
important advantage of this estimator is that it does not require knowledge
of the rank or an upper bound on the nuclear norm of the unknown matrix
and, thus, it is adaptive. We provide lower bounds showing that our
estimator is minimax optimal. An efficient algorithm based on lifted coordinate
gradient descent is proposed to compute the estimator. A limited
Monte-Carlo experiment, using both simulated and real data is provided to
support our claims.
KLOPP, O., LAFOND, J., MOULINES, E. et SALMON, J. (2015). Adaptive Multinomial Matrix Completion. The Electronic Journal of Statistics, 9(2), pp. 2950-2975.