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2017 JMLR JMLR 2017

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

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. [abs] [ pdf ][ bib ] [ code ] [ r-project.org ] © JMLR 2017. (edit, beta)

🧭 Keyword Pioneer — multi-source datum
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning

Authors

Eemeli Leppäaho , Muhammad Ammad-ud-din , Samuel Kaski

Topics

Machine Learning > Core Methods > Clustering Machine Learning > Core Methods > Representation Learning

Keywords

latent factor sparse prior multi-source datum group factor analysis
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