Graphical multi-way models

Ilkka Huopaniemi, Tommi Suvitaival, Matej Oresic, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

3 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases. ECML PKDD 2010
PublisherSpringer
Pages538-553
ISBN (Electronic)978-3-642-15880-3
ISBN (Print)978-3-642-15879-7
DOIs
Publication statusPublished - 2010
MoE publication typeA4 Article in a conference publication
EventJoint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2010 - Barcelona, Spain
Duration: 20 Sep 201024 Sep 2010

Publication series

SeriesLecture Notes in Computer Science
Volume6321
ISSN0302-9743

Conference

ConferenceJoint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2010
Abbreviated titleECML PKDD
CountrySpain
CityBarcelona
Period20/09/1024/09/10

Fingerprint

learning
alignment

Keywords

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

Cite this

Huopaniemi, I., Suvitaival, T., Oresic, M., & Kaski, S. (2010). Graphical multi-way models. In Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010 (pp. 538-553). Springer. Lecture Notes in Computer Science, Vol.. 6321 https://doi.org/10.1007/978-3-642-15880-3_40
Huopaniemi, Ilkka ; Suvitaival, Tommi ; Oresic, Matej ; Kaski, Samuel. / Graphical multi-way models. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Springer, 2010. pp. 538-553 (Lecture Notes in Computer Science, Vol. 6321).
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Huopaniemi, I, Suvitaival, T, Oresic, M & Kaski, S 2010, Graphical multi-way models. in Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Springer, Lecture Notes in Computer Science, vol. 6321, pp. 538-553, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2010, Barcelona, Spain, 20/09/10. https://doi.org/10.1007/978-3-642-15880-3_40

Graphical multi-way models. / Huopaniemi, Ilkka; Suvitaival, Tommi; Oresic, Matej; Kaski, Samuel.

Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Springer, 2010. p. 538-553 (Lecture Notes in Computer Science, Vol. 6321).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Huopaniemi I, Suvitaival T, Oresic M, Kaski S. Graphical multi-way models. In Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2010. Springer. 2010. p. 538-553. (Lecture Notes in Computer Science, Vol. 6321). https://doi.org/10.1007/978-3-642-15880-3_40