Abstract
One drawback of Procrustes Analysis is the lack of robustness. To overcome this limitation a procedure that applies the Generalised Procrustes methods, by way of a progressive sequence inspired to the “forward search”, was developed. Starting from an initial centroid, defined by the partial point configuration satisfying the LMS principle, this is extended by joining, at every step, a restricted subset of the remaining points. At every insertion, the updated centroid, redetermined by the new considered points, is compared with the previous by way of the common elements. If significant variations of the similarity transformation parameters occur, they reveal the presence of outliers or non stationary points among the new elements just inserted.
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Crosilla, F., Beinat, A. (2006). A Forward Search Method for Robust Generalised Procrustes Analysis. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_23
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DOI: https://doi.org/10.1007/3-540-35978-8_23
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