Non parametric feature discriminate analysis for high dimension
نویسندگان
چکیده
A method for the linear discrimination of non parametric binary classification is presented. It searches for the discriminate direction which maximizes the generalized Patrick-Fischer distance between the projected classconditional densities. The theoretical background is introduced with a new estimator using orthogonal function according to the Patrick-Fischer distance that gives the best scalar and multivariate extractor. The application of this method to the classification of some binary real data set leads to results better than those based on the traditional linear discriminate analysis (LDA) and the recursive Kernel estimator of the Patrick-Fischer distance.
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