EXACT PRINCIPAL COMPONENT INFLUENCE MEASURES APPLIED TO THE ANALYSIS OF SPECTROSCOPIC DATA ON RICE

Exact influence measures are applied in the evaluation of a principal component decomposition for high;dimensional data. Some data used for classifying samples of rice from their near infra-red transmission pro files, following a preliminary principal component analysis, are exami ned in detail. A normalization of eigenvalue influence statistics is p roposed which ensures that measures reflect the relative orientations of observations, rather than their overall Euclidean distance from the sample mean. Thus, the analyst obtains more information from an analy sis of eigenvalues than from approximate approaches to eigenvalue infl uence. This is particularly important for high dimensional data where a complete investigation of eigenvector perturbations may be cumbersom e. The results are used to suggest a new class of influence measures b ased on ratios of Euclidean distances in orthogonal spaces.