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Title: A least squares approach to Principal Component Analysis for interval valued data
Authors: D'Urso, Pierpaolo
Giordani, Paolo
Keywords: Principal Component Analysis
Least squares approach
Interval valued data
Chemical data
Issue Date: 4-Nov-2003
Abstract: Principal Component Analysis (PCA) is a well known technique the aim of which is to synthesize huge amounts of numerical data by means of a low number of unobserved variables, called components. In this paper, an extension of PCA to deal with interval valued data is proposed. The method, called Midpoint Radius Principal Component Analysis (MR-PCA) recovers the underlying structure of interval valued data by using both the midpoints (or centers) and the radii (a measure of the interval width) in
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