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.
Cite
Text
Huopaniemi et al. "Graphical Multi-Way Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15880-3_40Markdown
[Huopaniemi et al. "Graphical Multi-Way Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/huopaniemi2010ecmlpkdd-graphical/) doi:10.1007/978-3-642-15880-3_40BibTeX
@inproceedings{huopaniemi2010ecmlpkdd-graphical,
title = {{Graphical Multi-Way Models}},
author = {Huopaniemi, Ilkka and Suvitaival, Tommi and Oresic, Matej and Kaski, Samuel},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {538-553},
doi = {10.1007/978-3-642-15880-3_40},
url = {https://mlanthology.org/ecmlpkdd/2010/huopaniemi2010ecmlpkdd-graphical/}
}