B0595
Title: Variable selection for clustering of three-way data
Authors: Mackenzie Neal - McMaster University (Canada) [presenting]
Paul McNicholas - McMaster University (Canada)
Abstract: Ample work on dimension reduction for multivariate model-based clustering has been conducted; however, to date, relatively few dimension reduction methods have been presented in the matrix variate paradigm. Such work is, for example, useful for modelling data arising from longitudinal studies with multiple responses or multivariate repeated measures data. As is commonly demonstrated in multivariate clustering, issues persist when clustering data with noisy and uninformative variables, these problems carry over to clustering three-way data. Thus, a variable selection algorithm for the matrix variate paradigm is presented and tested on real datasets.
