PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Two-Way Analysis of High-Dimensional Collinear Data
Ilkka Huopaniemi, Tommi Suvitaival, Janne Nikkilä, Matej Oresic and Samuel Kaski
Data Mining and Knowledge Discovery Volume 19, pp. 261-276, 2009.

Abstract

We present a Bayesian model for two-way ANOVA-type analysis of high-dimensional, small sample-size datasets with highly correlated groups of variables. Modern cellular measurement methods are a main application area; typically the task is differential analysis between diseased and healthy samples, complicated by additional covariates requiring a multi-way analysis. The main complication is the combination of high dimensionality and low sample size, which renders classical multivariate techniques useless. We introduce a hierarchical model which does dimensionality reduction by assuming that the input variables come in similarly-behaving groups, and performs an ANOVA-type decomposition for the set of reduced-dimensional latent variables. We apply the methods to study lipidomic profiles of a recent large-cohort human diabetes study.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6289
Deposited By:Samuel Kaski
Deposited On:08 March 2010