Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15880-3_40 | ECML/PKDD (1) |
Keywords | Field | DocType |
large dimensionality p,multiple independent covariates,standard multi-way model,recent graphical multi-way model,unknown alignment,unknown structure,experimental data,multivariate multi-way anova-type model,large p,samples n,anova,latent variable model,data integration | Data integration,Small number,Covariate,Experimental data,Multiview learning,Multivariate statistics,Computer science,Curse of dimensionality,Biomedicine,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | ISBN |
6321 | 0302-9743 | 3-642-15879-X |
Citations | PageRank | References |
1 | 0.38 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ilkka Huopaniemi | 1 | 26 | 2.41 |
| Tommi Suvitaival | 2 | 33 | 3.21 |
| Matej Oresic | 3 | 323 | 34.23 |
| Samuel Kaski | 4 | 2755 | 245.52 |