Abstrakti
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.
| Alkuperäiskieli | Englanti |
|---|---|
| Artikkeli | 39 |
| Sivut | 1-5 |
| Sivumäärä | 5 |
| Julkaisu | Journal of Machine Learning Research |
| Vuosikerta | 18 |
| Tila | Julkaistu - 2017 |
| OKM-julkaisutyyppi | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä |
Rahoitus
This work was financially supported by the Academy of Finland (Finnish Center of Excellence in Computational Inference Research COIN; grants 295503 and 292337 to MA and SK). We acknowledge the computational resources provided by Aalto Science-IT project.