GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
| dc.contributor | Aalto-yliopisto | fi |
| dc.contributor | Aalto University | en |
| dc.contributor.author | Leppäaho, Eemeli | en_US |
| dc.contributor.author | Ammad-ud-din, Muhammad | en_US |
| dc.contributor.author | Kaski, Samuel | en_US |
| dc.contributor.department | Department of Computer Science | en |
| dc.contributor.groupauthor | Centre of Excellence in Computational Inference, COIN | en |
| dc.contributor.groupauthor | Professorship Kaski Samuel | en |
| dc.contributor.groupauthor | Helsinki Institute for Information Technology (HIIT) | en |
| dc.contributor.groupauthor | Probabilistic Machine Learning | en |
| dc.date.accessioned | 2017-08-03T12:09:19Z | |
| dc.date.available | 2017-08-03T12:09:19Z | |
| dc.date.issued | 2017 | en_US |
| dc.description.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. | en |
| dc.description.version | Peer reviewed | en |
| dc.format.extent | 5 | |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.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 > | en |
| dc.identifier.issn | 1532-4435 | |
| dc.identifier.issn | 1533-7928 | |
| dc.identifier.other | PURE UUID: 7cdd1e87-9dbd-4243-85f8-28defd293c3c | en_US |
| dc.identifier.other | PURE ITEMURL: https://research.aalto.fi/en/publications/7cdd1e87-9dbd-4243-85f8-28defd293c3c | en_US |
| dc.identifier.other | PURE LINK: http://jmlr.org/papers/v18/16-509.html | en_US |
| dc.identifier.other | PURE LINK: http://www.jmlr.org/papers/volume18/16-509/16-509.pdf | en_US |
| dc.identifier.other | PURE FILEURL: https://research.aalto.fi/files/14254602/16_509.pdf | en_US |
| dc.identifier.uri | https://aaltodoc.aalto.fi/handle/123456789/27390 | |
| dc.identifier.urn | URN:NBN:fi:aalto-201708036358 | |
| dc.language.iso | en | en |
| dc.publisher | Microtome Publishing | |
| dc.relation.ispartofseries | Journal of Machine Learning Research | en |
| dc.relation.ispartofseries | Volume 18, pp. 1-5 | en |
| dc.rights | openAccess | en |
| dc.subject.keyword | Bayesian latent variable modelling | en_US |
| dc.subject.keyword | biclustering | en_US |
| dc.subject.keyword | data integration | en_US |
| dc.subject.keyword | factor analysis | en_US |
| dc.subject.keyword | multi-view learning | en_US |
| dc.title | GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis | en |
| dc.type | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä | fi |
| dc.type.version | publishedVersion |