  We consider the low-rank tensor train completion problem when additional side
information is available in the form of subspaces that contain the mode-$k$
fiber spans. We propose an algorithm based on Riemannian optimization to solve
the problem. Numerical experiments show that the proposed algorithm requires
far fewer known entries to recover the tensor compared to standard tensor train
completion methods.
