Principal component analysis on interval data
type: Item
instance of: scholarly article
title: Principal component analysis on interval data
zbMATH Open document ID: 1113.62072
author: Frederica Gioia
author: Cardo N. Lauro
published in: Computational Statistics
publication date: 2007-05-29T00:00:00Z
review text: The main ideas of principal component analysis (PCA) for multivariate interval-valued observations are discussed. Concepts of interval eigenvalues and singular values, interval eigenvectors, and interval correlation matrices are discussed. The principal components for interval data are defined as interval eigenvectors of the interval correlation matrix. Representation and interpretation of interval PCA results are discussed. Analysis of an oil data set is presented as an example.
Mathematics Subject Classification ID: 62H25
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 5158604
zbMATH Keywords: symbolic data analysis
zbMATH Keywords: correlation interval matrix
zbMATH Keywords: interval eigenvalue
zbMATH Keywords: interval algebra
MaRDI profile type: Publication
full work available at URL: s00180-006-0267-6
OpenAlex ID: W2024202062
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DOI: 10.1007/S00180-006-0267-6
description: scientific article; zbMATH DE number 5158604