Matrix factorization into the product of low-rank matrices induces non-identifiability, i.e., the mapping between the target matrix and factorized matrices is not one-to-one. In this paper, we theoretically investigate the influence of non-identifiability on Bayesian matrix factorization. More specifically, we show that a variational Bayesian method involves regularization effect even when the prior is non-informative, which is intrinsically different from the maximum a posteriori approach. We also extend our analysis to hierarchical Bayes scenarios where hyperparameters are also learned from data.
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