Graphical multi-way models

Authors:
Ilkka Huopaniemi;Tommi Suvitaival;Matej Orešič;Samuel Kaski
Affiliations:
Aalto University School of Science and Technology, Department of Information and Computer Science, Helsinki Institute for Information Technology, Aalto, Finland;Aalto University School of Science and Technology, Department of Information and Computer Science, Helsinki Institute for Information Technology, Aalto, Finland;VTT Technical Research Centre of Finland, Espoo, Finland;Aalto University School of Science and Technology, Department of Information and Computer Science, Helsinki Institute for Information Technology, Aalto, Finland
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Year:
2010

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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) multiview learning with paired samples, (ii) one covariate is time with unknown alignment, and (iii) multi-view learning without paired samples.