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

被引：0

作者：

Leppaaho, Eemeli ^{[1
]}

Ammad-ud-din, Muhammad ^{[1
]}

Kaski, Samuel ^{[1
]}

机构：

[1] Aalto Univ, Dept Comp Sci, HIIT, POB 15400, FI-00076 Aalto, Finland

来源：

JOURNAL OF MACHINE LEARNING RESEARCH | 2017年 / 18卷

基金：

芬兰科学院;

关键词：

Bayesian latent variable modelling; biclustering; data integration; factor analysis; multi-view learning;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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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页数：5

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