Variational auto-encoder based Bayesian Poisson tensor factorization for sparse and imbalanced count data

Non-negative tensor factorization models enable predictive analysis on count data. Among them, Bayesian Poisson–Gamma models can derive full posterior distributions of latent factors and are less sensitive to sparse count data. However, current inference methods for these Bayesian models adopt restricted update rules for the posterior parameters. They also fail to share the update information to better cope with the data sparsity. Moreover,...