Preconditioned conjugate gradient algorithms for graph regularized matrix completion

Low-rank matrix completion is the problem of recovering the missing entries of a data matrix by using the assumption that a good low-rank approximation to the true matrix is possible. Much attention has been paid recently to exploiting correlations between the column/row entities through side information to improve the matrix completion quality. In this paper, we propose an efficient algorithm for solving the low-rank matrix completion with graph-based regularizers. Experiments on synthetic data show that our approach achieves significant speedup compared to the alternating minimization scheme.