Correlation visualization of high dimensional data using topographic maps

Abstract

Correlation analysis has always been a key technique for understanding data. However, traditional methods are only applicable on the whole data set, providing only global information on correlations. Correlations usually have a local nature and two variables can be directly and inversely correlated at different points in the same data set. This situation arises typically in nonlinear processes. In this paper we propose a method to visualize the distribution of local correlations along the whole data set using dimension reduction mappings. The ideas are illustrated through an artificial data example. © Springer-Verlag Berlin Heidelberg 2002.