Low-rank Matrix Recovery With Unknown Correspondence

We study a matrix recovery problem with unknown correspondence: given the observation matrix Mo=[A,tilde P B], where tilde P is an unknown permutation matrix, we aim to recover the underlying matrix M=[A,B]. Such problem commonly arises in many applications where heterogeneous data are utilized and the correspondence among them are unknown, e.g., due to privacy concerns. We show that it is possible to recover M via solving a nuclear norm minimization problem under a proper low-rank condition on M, with provable non-asymptotic error bound for the recovery of M. We propose an algorithm, M3O (Matrix recovery via Min-Max Optimization) which recasts this combinatorial problem as a continuous minimax optimization problem and solves it by proximal gradient with a Max-Oracle. M3O can also be applied to a more general scenario where we have missing entries in Mo and multiple groups of data with distinct unknown correspondence. Experiments on simulated data, the MovieLens 100K dataset and Yale B database show that M3O achieves state-of-the-art performance over several baselines and can recover the ground-truth correspondence with high accuracy.