Classification of form under heterogeneity and non-isotropic errors

Abstract

ABSTRACTA number of areas related to learning under supervision have not been fully investigated, particularly the possibility of incorporating the method of classification into shape analysis. In this regard, practical ideas conducive to the improvement of form classification are the focus of interest. Our proposal is to employ a hybrid classifier built on Euclidean Distance Matrix Analysis (EDMA) and Procrustes distance, rather than generalised Procrustes analysis (GPA). In empirical terms, it has been demonstrated that there is notable difference between the estimated form and the true form when EDMA is used as the basis for computation. However, this does not seem to be the case when GPA is employed. With the assumption that no association exists between landmarks, EDMA and GPA are used to calculate the mean form and diagonal weighting matrix to build superimposing classifiers. As our findings indicate, with the use of EDMA estimators, the superimposing classifiers we propose work extremely well, as opposed to the use of GPA, as far as both simulated and real datasets are concerned.