Before revision:  Its Bayesian nature completely avoids the ubiquitous in regularization-based methods task of hyper-parameter tuning. Simulation results with synthetic data are reported , which demonstrate the effectiveness of the proposed scheme in terms of both rank estimation and model fitting  .
After revision:  Its Bayesian nature completely avoids the ubiquitous in regularization-based methods . Alternative probabilistic models are also explored and the connections with their regularization-based counterparts are brought to light with the aid of the associated maximum a-posteriori (MAP) estimators. We report simulation results with both synthetic and real-word data , which demonstrate the effectiveness of the proposed scheme in terms of both rank estimation and model fitting .