In this paper, we provide an overview on the underlying response variable (URV) model-
based approach to cluster and, optionally, simultaneously reduce ordinal and, optionally …

With the scale of information growing every day, the key challenges in machine learning
include the high-dimensionality and sheer volume of feature vectors that may consist of real …

The goal of this project was to develop biologically appropriate mathematical models for
genotyping and patient data and use them to analyze and exploit the information in …

Abstract The” layered image model”[13] represents an image sequence as a composition of
2D layers where each layer corresponds to a different object. A layer is described by its …

Principal component analysis is a widely used technique for dimensionality reduction, but is
not based on a probability model. Many recently proposed dimension reduction methods are …

We investigate a generalized linear model for dimensionality reduction of binary data. The
model is related to principal component analysis (PCA) in the same way that logistic …

Land cover maps are one of the primary kinds of geospatial information provided by remote
sensing. Land cover classification is essential for terrestrial ecosystem modeling and …

The question of how best to optimise the accuracy of genetic evaluation for livestock
populations has been given new life by the advent of genomics. Therefore we will …

Latent linear models are core to much of machine learning and statistics. Specific examples
of this model class include Bayesian generalised linear models, Gaussian process …

The mixture models behave very well to cluster large samples of continuous or categorical
data. Adding a vicinity constraint permits them to project data like factorial methods but in a …