Graphical Multi-Way Models

Reference:

Ilkka Huopaniemi, Tommi Suvitaival, Matej Orešic, and Samuel Kaski. Graphical multi-way models. In Aristides Gionis José Luis Balcázar, Francesco Bonchi and Mich`ele Sebag, editors, Machine Learning and Knowledge Discovery in Databases. Proceedings of European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, volume I, pages 538–553, Berlin, 2010. Springer.

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.

Suggested BibTeX entry:

@inproceedings{Huopaniemi10ecml,
    address = {Berlin},
    author = {Ilkka Huopaniemi and Tommi Suvitaival and Matej Ore{\v{s}}i{\v{c}} and Samuel Kaski},
    booktitle = {Machine Learning and Knowledge Discovery in Databases. Proceedings of European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010},
    editor = {Jos{\'e} Luis Balc{\'a}zar, Francesco Bonchi, Aristides Gionis and Mich\{`e}le Sebag},
    pages = {538-553},
    publisher = {Springer},
    title = {Graphical Multi-Way Models},
    volume = {I},
    year = {2010},
}