Probabilistic tensor factorization method for mixed data

摘要

We study a tensor-based Bayesian probabilistic modeling of heterogeneously attributed multi-dimensional arrays each of which assumes a different exponential-family distribution. Simulation experiments show that our method outperforms other methods such as Tucker decomposition in missing-values prediction for cross-national statistics.