An improved majorization algorithm for Robust Procrustes analysis

Patrick J.F. Groenen, Patrizia Giaquinto, Henk A.L. Kiers

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

2 Citations (Scopus)

Abstract

In this paper, we focus on algorithms for Robust Procrustes Analysis that are used to rotate a solution of coordinates towards a target solution while controlling outliers. Verboon (1994) and Verboon and Heiser (1992) showed how iterative weighted least-squares can be used to solve the problem. Kiers (1997) improved upon their algorithm by using iterative majorization. In this paper, we propose a new method called “weighted majorization” that improves on the method by Kiers (1997). A simulation study shows that compared to the method by Kiers (1997), the solutions obtained by weighted majorization are in almost all cases of better quality and are obtained significantly faster.

Original languageEnglish
Title of host publicationStudies in Classification, Data Analysis, and Knowledge Organization
EditorsMaurizio Vichi, Paola Monari, Stefania Mignani, Angela Montanari
PublisherSpringer Science+Business Media
Pages151-158
Number of pages8
Edition211289
ISBN (Print)9783319557076, 9783319557229, 9783540238096
DOIs
Publication statusPublished - 2005
EventBiannual meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2003 - Bologna, Italy
Duration: 22 Sep 200324 Sep 2003

Publication series

SeriesStudies in Classification, Data Analysis, and Knowledge Organization
Number211289
Volume0
ISSN1431-8814

Conference

ConferenceBiannual meeting of the Classification and Data Analysis Group of the Italian Statistical Society, CLADAG 2003
Country/TerritoryItaly
CityBologna
Period22/09/0324/09/03

Bibliographical note

Publisher Copyright:
© 2005, Springer-Verlag. Heidelberg 2005.

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