A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction
Machine Learning
2014-10-01 v1 Numerical Analysis
Machine Learning
Abstract
Probabilistic approaches for tensor factorization aim to extract meaningful structure from incomplete data by postulating low rank constraints. Recently, variational Bayesian (VB) inference techniques have successfully been applied to large scale models. This paper presents full Bayesian inference via VB on both single and coupled tensor factorization models. Our method can be run even for very large models and is easily implemented. It exhibits better prediction performance than existing approaches based on maximum likelihood on several real-world datasets for missing link prediction problem.
Cite
@article{arxiv.1409.8276,
title = {A Bayesian Tensor Factorization Model via Variational Inference for Link Prediction},
author = {Beyza Ermis and A. Taylan Cemgil},
journal= {arXiv preprint arXiv:1409.8276},
year = {2014}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1409.8083