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L₂ Kernel Classification
October 2010 (vol. 32 no. 10)
pp. 1822-1831
JooSeuk Kim, University of Michigan, Ann Arbor
Clayton D. Scott, University of Michigan, Ann Arbor
Nonparametric kernel methods are widely used and proven to be successful in many statistical learning problems. Well--known examples include the kernel density estimate (KDE) for density estimation and the support vector machine (SVM) for classification. We propose a kernel classifier that optimizes the L_2 or integrated squared error (ISE) of a “difference of densities.” We focus on the Gaussian kernel, although the method applies to other kernels suitable for density estimation. Like a support vector machine (SVM), the classifier is sparse and results from solving a quadratic program. We provide statistical performance guarantees for the proposed L_2 kernel classifier in the form of a finite sample oracle inequality and strong consistency in the sense of both ISE and probability of error. A special case of our analysis applies to a previously introduced ISE-based method for kernel density estimation. For dimensionality greater than 15, the basic L_2 kernel classifier performs poorly in practice. Thus, we extend the method through the introduction of a natural regularization parameter, which allows it to remain competitive with the SVM in high dimensions. Simulation results for both synthetic and real-world data are presented.

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Index Terms:
Kernel methods, sparse classifiers, integrated squared error, difference of densities, SMO algorithm.
Citation:
JooSeuk Kim, Clayton D. Scott, "L₂ Kernel Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 10, pp. 1822-1831, Oct. 2010, doi:10.1109/TPAMI.2009.188
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