Learning subspace kernels for classification

Abstract

Kernel methods have been applied successfully in many data mining tasks. Subspace kernel learning was recently proposed to discover an effective low-dimensional subspace of a kernel feature space for improved classification. In this paper, we propose to construct a subspace kernel using the Hilbert-Schmidt Independence Criterion (HSIC). We show that the… (More)
DOI: 10.1145/1401890.1401908

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