Bayesian multi-tensor factorization methods, with side information
27 Stars
Updated Last
1 Year Ago
Started In
January 2015


Build Status

Implementation of data fusion methods in Julia, specifically Macau, BPMF (Bayesian Probabilistic Matrix Factorization). Supported features:

  • Factorization of matrices (without or with side information)
  • Factorization of tensors (without or with side information)
  • Co-factorization of multiple matrices and tensors (any side information is possible)
  • Side information inside relation
  • Parallelization (multi-core and multi-node)

BayesianDataFusion uses Gibbs sampling to learn the latent vectors and link matrices. In addition to predictions Gibbs sampling also provides estimates of standard deviation and possible other metrics (that can be computed from samples).



Examples and documentation

For examples, please see documentation.