TY - GEN
T1 - Graphical multi-way models
AU - Huopaniemi, Ilkka
AU - Suvitaival, Tommi
AU - Orešič, Matej
AU - Kaski, Samuel
PY - 2010/11/5
Y1 - 2010/11/5
N2 - 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.
AB - 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.
KW - ANOVA
KW - Bayesian latent variable modeling
KW - data integration
KW - multi-view learning
KW - multi-way learning
UR - http://www.scopus.com/inward/record.url?scp=78049350804&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15880-3_40
DO - 10.1007/978-3-642-15880-3_40
M3 - Article in proceedings
AN - SCOPUS:78049350804
SN - 364215879X
SN - 9783642158797
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 538
EP - 553
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2010, Proceedings
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2010
Y2 - 20 September 2010 through 24 September 2010
ER -