📝 SummarXiv

Abstract

Canonical Variate Analysis (CVA) is a multivariate statistical technique and a direct application of Linear Discriminant Analysis (LDA) that aims to find linear combinations of variables that best differentiate between groups in a dataset. The data is partitioned into groups based on some predetermined criteria, and then linear combinations of the original variables are derived such that they maximize the separation between the groups. However, a common limitation of this optimization in CVA is that the within cluster scatter matrix must be nonsingular, which restricts the use of datasets when the number of variables is larger than the number of observations. By applying the generalized singular value decomposition (GSVD), the same goal of CVA can be achieved regardless on the number of variables. In this paper we use this approach to show that CVA can be applied and graphical representations to such data can be constructed. Specifically, we will be looking at the construction of a CVA biplot for such data that will display observations as points and variables as axes in a reduced dimension. Finally, we present experimental results that confirm the effectiveness of our approach.