Multivariate data analysis finds patterns in high dimensionality data by examining the interrelationships of the multivariate data set. Multivariate techniques can be employed to analyse complex data sets involving longitudinal data, spatial data (e.g. structural image of retina) and functional data (e.g. function of eye via visual acuity test). The most commonly applied multivariate techniques are discriminant function analysis, cluster analysis, principal components analysis, factor analysis, multidimensional scaling and MANOVA amongst others.
Some of our current work and applications include:
- Methods for discrimination between two or more disease groups when the groups show different variability
- Algorithms for sample size calculations for linear discriminant analyses
- Evaluation of how good the classifiers are
- Development of dimension reduction techniques for selecting features without losing important information that can affect classification accuracy
- Applications to the clinical areas of diabetes, cancer research, brain imaging, stem cell research, ophthalmology (e.g., classification of patients with diabetes into diabetic retinopathy stages for early diagnosis)