GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2017
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Language
en
Pages
5
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Journal of Machine Learning Research, Volume 18, pp. 1-5
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.Description
Keywords
Bayesian latent variable modelling, biclustering, data integration, factor analysis, multi-view learning
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Citation
Leppäaho, E, Ammad-ud-din, M & Kaski, S 2017, ' GFA : Exploratory Analysis of Multiple Data Sources with Group Factor Analysis ', Journal of Machine Learning Research, vol. 18, 39, pp. 1-5 . < http://jmlr.org/papers/v18/16-509.html >