Open Universiteit

Please use this identifier to cite or link to this item:
Title: Resistant lower rank approximation of matrices by iterative majorization
Authors: Verboon, Peter
Heiser, Willem
Keywords: lower rank approximation
huber function
biweight function
Issue Date: 1994
Publisher: Elsevier Science B.V.
Citation: Computational Statistics & Data Analysis 18 (1994) 457-467
Abstract: It is commonly known that many techniques for data analysis based on the least squares criterion are very sensitive to outliers in the data. Gabriel and Odoroff (1984) suggested a resistant approach for lower rank approximation of matrices. In this approach, weights are used to diminish the influence of outliers on the low-dimensional representation. The present paper uses iterative majorization to provide for a general algorithm for such resistant lower rank approximations which guarantees convergence. It is shown that the weights can be chosen in different ways corresponding with different objective functions. Some possible extensions of the algorithm are discussed.
Appears in Collections:1. PSY: publications and preprints

Files in This Item:
File Description SizeFormat 
Verboon & Heiser, 1994 CSDA.pdf725.05 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.