A unifying review of linear Gaussian models
Neural Computation
Proceedings of the 1998 conference on Advances in neural information processing systems II
Bioinformatics
Guest editors' introduction: special issue of selected papers from ECML PKDD 2009
Data Mining and Knowledge Discovery
Guest editors' introduction: Special Issue from ECML PKDD 2009
Machine Learning
Two-Way Analysis of High-Dimensional Collinear Data
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Cross-species translation of multi-way biomarkers
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Bayesian Canonical correlation analysis
The Journal of Machine Learning Research
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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.