@inproceedings{ce5072e6907b46d4841eabc1ad22593f,
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 Ore{\v s}i{\v c} and Samuel Kaski",
year = "2010",
month = nov,
day = "5",
doi = "10.1007/978-3-642-15880-3\_40",
language = "English",
isbn = "364215879X",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
number = "PART 1",
pages = "538--553",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings",
edition = "PART 1",
note = "European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010 ; Conference date: 20-09-2010 Through 24-09-2010",
}