  We study the problem of learning mixtures of low-rank models, i.e.
reconstructing multiple low-rank matrices from unlabelled linear measurements
of each. This problem enriches two widely studied settings -- low-rank matrix
sensing and mixed linear regression -- by bringing latent variables (i.e.
unknown labels) and structural priors (i.e. low-rank structures) into
consideration. To cope with the non-convexity issues arising from unlabelled
heterogeneous data and low-complexity structure, we develop a three-stage
meta-algorithm that is guaranteed to recover the unknown matrices with
near-optimal sample and computational complexities under Gaussian designs. In
addition, the proposed algorithm is provably stable against random noise. We
complement the theoretical studies with empirical evidence that confirms the
efficacy of our algorithm.
