  Tensor completion and tensor decomposition are important problems in many
domains. In this work, we leverage the connection between these problems to
learn a distance metric that improves both decomposition and completion. We
show that the optimal Mahalanobis distance metric for the completion task is
closely related to the Tucker decomposition of the completed tensor. Then, we
formulate a bilevel optimization problem to perform joint tensor completion and
decomposition, subject to metric learning constraints. The metric learning
constraints also allow us to flexibly incorporate similarity side information
and coupled matrices, when available, into the tensor recovery process. We
derive an algorithm to solve the bilevel optimization problem and prove its
global convergence. When evaluated on real data, our approach performs
significantly better compared to previous methods.
