Abstract
I propose a modification of exploratory projection pursuit which trades accuracy for interpretability in the resulting description. Interpretability, a generalization of parsimony, is based on the ideas of rotation in factor analysis and of entropy. It is defined as the simplicity of the coefficients which specify the description’s projections. A weighted optimization approach similar to roughness penalty curve-fitting is used to search for a more understandable description, with interpretability replacing smoothness. A real data example is presented. The method retains the nonlinear versatility of projection pursuit but has more intuitive appeal.
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© 1992 Springer-Verlag New York, Inc.
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Morton, S.C. (1992). Interpretable Exploratory Projection Pursuit. In: Page, C., LePage, R. (eds) Computing Science and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2856-1_79
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DOI: https://doi.org/10.1007/978-1-4612-2856-1_79
Publisher Name: Springer, New York, NY
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