Abstract
In this paper a brief review of the main contributions to projection pursuit discriminant analysis is presented. A new procedure, based on distance measures between probability densities, is developed in order to obtain linear discriminant functions, which best separate different populations. In the two- group case, Matusita’s distance between the projected population density functions is adopted as projection index; while, in the multi-group case, a monotone transformation of Matusita’s affinity coefficient is employed to measure the separation among the marginal probability density functions of the different populations. Simulation studies stress the efficacy of the proposed method in comparison with classical parametric ones and with the projection pursuit based linear discriminant procedure developed by Posse.
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Lizzani, L. (1998). A Density Distance Based Approach to Projection Pursuit Discriminant Analysis. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_34
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DOI: https://doi.org/10.1007/978-3-642-72253-0_34
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