Twinned principal curves

Authors:
Jos Koetsier;Ying Han;Colin Fyfe
Affiliations:
Applied Computational Intelligence Research Unit, The University of Paisley, Paisley, Scotland, UK;Applied Computational Intelligence Research Unit, The University of Paisley, Paisley, Scotland, UK;Applied Computational Intelligence Research Unit, The University of Paisley, Paisley, Scotland, UK
Venue:
Neural Networks
Year:
2004

Citing 6
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Abstract

Principal Curves are extensions of Principal Component Analysis and are smooth curves, which pass through the middle of a data set. We extend the method so that, on pairs of data sets which have underlying non-linear correlations, we have pairs of curves which go through the 'centre' of data sets in such a way that the non-linear correlations between the data sets are captured. The core of the method is to iteratively average the current local projections of the data points which produces an increasingly sparsified set of nodes. The Twinned Principal Curves are generated in three ways: by joining up the nodes in order, by performing Local Canonical Correlation Analysis and by performing Local Exploratory Correlation Analysis (Koetsier et al., 2002). The latter two are shown to improve the forecasting capability of the method but at an increased computational load. We show that it is crucial to terminate the algorithm after a small number of iterations for the first method and investigate several criteria for doing so.