Graphical Multi-way Models

  • Ilkka Huopaniemi
  • Tommi Suvitaival
  • Matej Orešič
  • Samuel Kaski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6321)


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.


ANOVA Bayesian latent variable modeling data integration multi-view learning multi-way learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ilkka Huopaniemi
    • 1
  • Tommi Suvitaival
    • 1
  • Matej Orešič
    • 2
  • Samuel Kaski
    • 1
  1. 1.Department of Information and Computer Science, Helsinki Institute for, Information Technology HIITAalto University School of Science and TechnologyAaltoFinland
  2. 2.VTT Technical Research Centre of FinlandVTT, EspooFinland

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