ML Anthology

GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis

Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski
MLOSS 2017 pp. 1-5
/mloss/2017/leppaaho2017jmlr-gfa/

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.

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Cite

Text

Leppäaho et al. "GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis." Machine Learning Open Source Software, 2017.

Markdown

[Leppäaho et al. "GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis." Machine Learning Open Source Software, 2017.](https://mlanthology.org/mloss/2017/leppaaho2017jmlr-gfa/)

BibTeX

@article{leppaaho2017jmlr-gfa,
  title     = {{GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis}},
  author    = {Leppäaho, Eemeli and Ammad-ud-din, Muhammad and Kaski, Samuel},
  journal   = {Machine Learning Open Source Software},
  year      = {2017},
  pages     = {1-5},
  volume    = {18},
  url       = {https://mlanthology.org/mloss/2017/leppaaho2017jmlr-gfa/}
}