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.
CITATION STYLE
Groenen, P. J. F., Giaquinto, P., & Kiers, H. A. L. (2005). An improved majorization algorithm for Robust Procrustes analysis. In Studies in Classification, Data Analysis, and Knowledge Organization (Vol. 0, pp. 151–158). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/3-540-27373-5_18
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