Principal Component Analysis for Interval-Valued Observations

Abstract : One feature of contemporary datasets is that instead of the single point value in the p-dimensional space p seen in classical data, the data may take interval values thus producing hypercubes in p. This paper studies the vertices principal components methodology for interval-valued data; and provides enhancements to allow for so-called 'trivial' intervals, and generalized weight functions. It also introduces the concept of vertex contributions to the underlying principal components, a concept not possible for classical data, but one which provides a visualization method that further aids in the interpretation of the methodology. The method is illustrated in a dataset using measurements of facial characteristics obtained from a study of face recognition patterns for surveillance purposes. A comparison with analyses in which classical surrogates replace the intervals, shows how the symbolic analysis gives more informative conclusions. A second example illustrates how the method can be applied even when the number of parameters exceeds the number of observations, as well as how uncertainty data can be accommodated.  2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 229-246, 2011
Type de document :
Article dans une revue
Statistical Analysis and Data Mining, 2011, 4 (2), pp.229-246. 〈10.1002/sam.10118〉
Liste complète des métadonnées

Littérature citée [40 références]  Voir  Masquer  Télécharger
Contributeur : Edwin Diday <>
Soumis le : mardi 13 novembre 2012 - 16:15:39
Dernière modification le : jeudi 7 février 2019 - 15:47:48
Document(s) archivé(s) le : jeudi 14 février 2013 - 02:25:08


Fichiers produits par l'(les) auteur(s)




Ahlame Douzal-Chouakria, Lynne Billard, Edwin Diday. Principal Component Analysis for Interval-Valued Observations. Statistical Analysis and Data Mining, 2011, 4 (2), pp.229-246. 〈10.1002/sam.10118〉. 〈hal-00659996〉



Consultations de la notice


Téléchargements de fichiers