Regularized Generalized Canonical Correlation Analysis Extended to Symbolic Data

Regularized Generalized Canonical Correlation Analysis (RGCCA) is a component-based approach which aims at studying the relationship between several blocks of numerical variables. In this paper we propose a method called Symbolic Generalized Canonical Correlation Analysis (Symbolic GCCA) that extends RGCCA to symbolic data. It is a versatile tool for multi-block data analysis that can deal with any type of datasets (e.g. observations described by intervals, histograms,...) provided that a relevant kernel function is defined for each block. A monotonically convergent algorithm for symbolic GCCA is presented and applied to a 4-block dataset of power plants cooling towers described by histograms.