  The recovery of a low rank matrix from a subset of noisy low-precision
quantized samples arises in several applications such as collaborative
filtering, intelligent recommendation and millimeter wave channel estimation
with few bit ADCs. In this paper, a generalized sparse Bayesian learning
(Gr-SBL) combining expectation propagation (EP) is proposed to solve the matrix
completion (MC), which is termed as MC-Gr-SBL. The MC-Gr-SBL automatically
estimates the rank, the factors and their covariance matrices, and the noise
variance. In addition, MC-Gr-SBL is proposed to solve the two dimensional line
spectral estimation problem by incorporating the MUSIC algorithm. Finally,
substantial numerical experiments are conducted to verify the effectiveness of
the proposed algorithm.
