Abstract
The R package GFA provides a full pipeline for factor analysis of multiple data sources that are represented as matrices with co-occurring samples. It allows learning dependencies between subsets of the data sources, decomposed into latent factors. The package also implements sparse priors for the factorization, providing interpretable biclusters of the multi-source data.
| Original language | English |
|---|---|
| Article number | 39 |
| Pages (from-to) | 1-5 |
| Number of pages | 5 |
| Journal | Journal of Machine Learning Research |
| Volume | 18 |
| Publication status | Published - 2017 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- Bayesian latent variable modelling
- biclustering
- data integration
- factor analysis
- multi-view learning