Distance-Based Classifier via the Kernel Trick
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Hongxin Zhang. Distance-Based Classifier via the Kernel Trick. International Journal of Software and Informatics, 2010,4(2):121~133
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Fund:This work is supported in part by the National Natural Science Foundation of China under GrantNo.61070073.
Abstract:For classifying large data sets, we propose a discriminant kernel that introduces a nonlinear mapping from the joint space of input data and output label to a discriminant space. Our method differs from traditional ones, which correspond to map nonlinearly from the input space to a feature space. The induced distance of our discriminant kernel is Eu- clidean and Fisher separable, as it is defined based on distance vectors of the feature space to distance vectors on the discriminant space. Unlike the support vector machines or the kernel Fisher discriminant analysis, the classifier does not need to solve a quadric program- ming problem or eigen-decomposition problems. Therefore, it is especially appropriate to the problems of processing large data sets. The classifier can be applied to face recognition, shape comparison and image classification benchmark data sets. The method is significantly faster than other methods and yet it can deliver comparable classification accuracy.
keywords:pattern classification  fisher separable  kernel trick
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