Graphical multi-way models

Ilkka Huopaniemi*, Tommi Suvitaival, Matej Orešič, Samuel Kaski

*Corresponding author af dette arbejde
3 Citationer (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.

OriginalsprogEngelsk
TitelMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings
Antal sider16
Publikationsdato5 nov. 2010
UdgavePART 1
Sider538-553
ISBN (Trykt)364215879X, 9783642158797
DOI
StatusUdgivet - 5 nov. 2010
Udgivet eksterntJa
BegivenhedEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010 - Barcelona, Spanien
Varighed: 20 sep. 201024 sep. 2010

Konference

KonferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010
Land/OmrådeSpanien
ByBarcelona
Periode20/09/201024/09/2010
SponsorFr. Natl. Inst. Res. Comput. Sci. Control (INRIA), Pascal2 European Network of Excellence, Nokia, Yahoo Labs, Google
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NummerPART 1
Vol/bind6321 LNAI
ISSN0302-9743

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