Bayesian multi-tensor factorization

详细信息    查看全文

• 作者：Suleiman A. Khan ; Eemeli Leppäaho ; Samuel Kaski
• 关键词：Bayesian factorization ; CANDECOMP/PARAFAC ; Coupled matrix tensor factorization ; Factor analysis ; Tensor factorization
• 刊名：Machine Learning
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：105
• 期：2
• 页码：233-253
• 全文大小：1,037 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Automation and Robotics
  Computing Methodologies
  Simulation and Modeling
  Language Translation and Linguistics
  
• 出版者：Springer Netherlands
• ISSN：1573-0565
• 卷排序：105

文摘

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.