Classification methods for providing personalised and class decisions (2018–2021)

Abstract:
We shall develop a novel and widely applicable mixture model-based framework for the simultaneous clustering of multivariate samples with inter-sample variation in a class and for the matching of the clusters across the entities in the class. This statistical approach provides an automatic way for matching the clusters since the overall mixture model provides a template for the class. It can be used to provide a basis for discriminating between different classes in addition to the identification of atypical data points within a sample and of anomalous samples within a class. Key applications include biological image analysis and the analysis of data in flow cytometry which is one of the fundamental research tools for the life scientist.
Grant type:
ARC Discovery Projects
Researchers:
Funded by:
Australian Research Council