Algorithms for Projection-Pursuit Robust Principal Component Analysis

KU Leuven Working Paper No. KBI 0624

18 Pages Posted: 8 Mar 2007

See all articles by Christophe Croux

Christophe Croux

KU Leuven - Faculty of Business and Economics (FEB)

Peter Filzmoser

Vienna University of Technology

M. Rosario Oliveira

Instituto Superior Técnico

Date Written: 2006

Abstract

Principal Component Analysis (PCA) is very sensitive in presence of outliers. One of the most appealing robust methods for principal component analysis uses the Projection-Pursuit principle. Here, one projects the data on a lower-dimensional space such that a robust measure of variance of the projected data will be maximized. The Projection-Pursuit based method for principal component analysis has recently been introduced in the field of chemometrics, where the number of variables is typically large. In this paper, it is shown that the currently available algorithm for robust Projection-Pursuit PCA performs poor in presence of many variables. A new algorithm is proposed that is more suitable for the analysis of chemical data. Its performance is studied by means of simulation experiments and illustrated on some real datasets.

Keywords: Algorithms, Data, Field, IT, Methods, Outliers, Performance, Principal component analysis, Principal components analysis, Projection-pursuit, Robustness, Simulation, Space, Variables, Variance

Suggested Citation

Croux, Christophe and Filzmoser, Peter and Oliveira, M. Rosario, Algorithms for Projection-Pursuit Robust Principal Component Analysis (2006). KU Leuven Working Paper No. KBI 0624, Available at SSRN: https://ssrn.com/abstract=968376 or http://dx.doi.org/10.2139/ssrn.968376

Christophe Croux (Contact Author)

KU Leuven - Faculty of Business and Economics (FEB) ( email )

Naamsestraat 69
Leuven, B-3000
Belgium

Peter Filzmoser

Vienna University of Technology ( email )

Wien 1040
Austria

M. Rosario Oliveira

Instituto Superior Técnico ( email )

Lisbon
Portugal

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