Bayesian Matrix Factorization with Non-Random Missing Data Using Informative Gaussian Process Priors and Soft Evidences
Abstract
We propose an extended Bayesian matrix factorization method, which can incorporate multiple sources of side information, combine multiple \empha priori estimates for the missing data and integrates a flexible missing not at random submodel. The model is formalized as probabilistic graphical model and a corresponding Gibbs sampling scheme is derived to perform unrestricted inference. We discuss the application of the method for completing drug–target interaction matrices, also discussing specialties in this domain. Using real-world drug–target interaction data, the performance of the method is compared against both a general Bayesian matrix factorization method and a specific one developed for drug–target interaction prediction. Results demonstrate the advantages of the extended model.
Cite
Text
Bolgár and Antal. "Bayesian Matrix Factorization with Non-Random Missing Data Using Informative Gaussian Process Priors and Soft Evidences." Proceedings of the Eighth International Conference on Probabilistic Graphical Models, 2016.Markdown
[Bolgár and Antal. "Bayesian Matrix Factorization with Non-Random Missing Data Using Informative Gaussian Process Priors and Soft Evidences." Proceedings of the Eighth International Conference on Probabilistic Graphical Models, 2016.](https://mlanthology.org/pgm/2016/bolgar2016pgm-bayesian/)BibTeX
@inproceedings{bolgar2016pgm-bayesian,
title = {{Bayesian Matrix Factorization with Non-Random Missing Data Using Informative Gaussian Process Priors and Soft Evidences}},
author = {Bolgár, Bence and Antal, Péter},
booktitle = {Proceedings of the Eighth International Conference on Probabilistic Graphical Models},
year = {2016},
pages = {25-36},
volume = {52},
url = {https://mlanthology.org/pgm/2016/bolgar2016pgm-bayesian/}
}