Bayesian multi-tensor factorization
Research output: Contribution to journal › Article › Scientific › peer-review
We introduce Bayesian multi-tensor factorization, a model that is the first Bayesian formulation for joint factorization of multiple matrices and tensors. The research problem generalizes the joint matrix–tensor factorization problem to arbitrary sets of tensors of any depth, including matrices, can be interpreted as unsupervised multi-view learning from multiple data tensors, and can be generalized to relax the usual trilinear tensor factorization assumptions. The result is a factorization of the set of tensors into factors shared by any subsets of the tensors, and factors private to individual tensors. We demonstrate the performance against existing baselines in multiple tensor factorization tasks in structural toxicogenomics and functional neuroimaging.
|Number of pages||21|
|Early online date||10 Jun 2016|
|Publication status||Published - Nov 2016|
|MoE publication type||A1 Journal article-refereed|
- Bayesian factorization, CANDECOMP/PARAFAC, Coupled matrix tensor factorization, Factor analysis, Tensor factorization