Low-rank tensor completion using parallel matrix factorization with factor priors

      In this talk, we discuss the low-rank tensor completion (LRTC) problem.  To tackle the LRTC problem, we propose the parallel matrix factorization with factor priors (e.g., smoothness). We develop an efficient block successive upper-bound minimization method to solve the proposed model. The convergence of the numerical scheme is theoretically guaranteed under some mild conditions. Numerical examples of synthetic and real data are reported to demonstrate the effectiveness of the proposed model and the efficiency of the proposed numerical scheme.