@inproceedings{8c7973910e114f4faf28a807b535201d,
title = "Graphical multi-way models",
abstract = "Multivariate multi-way ANOVA-type models are the default tools for analyzing experimental data with multiple independent covariates. However, formulating standard multi-way models is not possible when the data comes from different sources or in cases where some covariates have (partly) unknown structure, such as time with unknown alignment. The “small n, large p”, large dimensionality p with small number of samples n, settings bring further problems to the standard multivariate methods. We extend our recent graphical multi-way model to three general setups, with timely applications in biomedicine: (i) multi-view learning with paired samples, (ii) one covariate is time with unknown alignment, and (iii) multi-view learning without paired samples.",
keywords = "ANOVA, Bayesian latent variable modeling, data integration, multi-view learning, multi-way learning",
author = "Ilkka Huopaniemi and Tommi Suvitaival and Matej Oresic and Samuel Kaski",
note = "CO:Aalto University CA2: TK400; Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2010, ECML PKDD ; Conference date: 20-09-2010 Through 24-09-2010",
year = "2010",
doi = "10.1007/978-3-642-15880-3_40",
language = "English",
isbn = "978-3-642-15879-7",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "538--553",
booktitle = "Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010",
address = "Germany",
}