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2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops (2005)
San Diego, California
June 20, 2005 to June 26, 2005
ISSN: 1063-6919
ISBN: 0-7695-2660-8
pp: 42
Herve Abdi , The University of Texas at Dallas
Alice J. O?Toole , The University of Texas at Dallas
Dominique Valentin , Universite de Bourgogne
Betty Edelman , The University of Texas at Dallas
ABSTRACT
<p>In this paper we present a generalization of classical multidimensional scaling called DISTATIS which is a new method that can be used to compare algorithms when their outputs consist of distance matrices computed on the same set of objects. The method first evaluates the similarity between algorithms using a coefficient called the RV coefficient. From this analysis, a compromise matrix is computed which represents the best aggregate of the original matrices. In order to evaluate the differences between algorithms, the original distance matrices are then projected onto the compromise. We illustrate this method with a "toy example" in which four different "algorithms" (two computer programs and two sets of human observers) evaluate the similarity among faces.</p>
INDEX TERMS
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CITATION

D. Valentin, A. J. O?Toole, H. Abdi and B. Edelman, "DISTATIS: The Analysis of Multiple Distance Matrices," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops(CVPRW), San Diego, California, 2005, pp. 42.
doi:10.1109/CVPR.2005.445
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