Kernel Subspace Learning for Pattern Classification
Yinan Yu⁎; Konstantinos Diamantaras†; Tomas McKelvey⁎; S.Y. Kung‡ ⁎Chalmers University of Technology, Electrical Engineering, Gothenburg, Sweden†TEI of Thessaloniki, Thessaloniki, Greece‡Princeton University, Princeton, NJ, United States
Abstract
Kernel methods are nonparametric feature extraction techniques that attempt to boost the learning capability of machine learning algorithms using nonlinear transformations. However, one major challenge in its basic form is that the computational complexity and the memory requirement do not scale well with respect to the training size. Kernel approximation is commonly employed to resolve this issue. Essentially, kernel approximation ...
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