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Enhanced Distance Subset Approximation using Class-Specific Subspace Kernel Representation for Kernel Approximation

Yinan Yu (Institutionen för signaler och system, Signalbehandling) ; Konstantinos Diamantaras ; Tomas McKelvey (Institutionen för signaler och system, Signalbehandling) ; S.Y. Kung
Proceeding of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016), Vietri sul Mare, Italy (2161-0363). Vol. 2016-November (2016),
[Konferensbidrag, refereegranskat]

The computational complexity of kernel methods grows at least quadratically with respect to the training size and hence low rank kernel approximation techniques are commonly used. One of the most popular approximations is constructed by sub-sampling the training data. In this paper, we present a sampling algorithm called Enhanced Distance Subset Approximation (EDSA) based on a novel kernel function called CLAss-Specific Kernel (CLASK), which applies the idea of subspace clustering to low rank kernel approximation. By representing the kernel matrix based on a class-specific subspace model, it is allowed to use distinct kernel functions for different classes, which provides a better flexibility compared to classical kernel approximation techniques. Experimental results conducted on various UCI datasets are provided in order to verify the proposed techniques.

Nyckelord: class-specific subspace model; classification; discriminative representation; Kernel approximation

Article number 7738811

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Denna post skapades 2016-10-17. Senast ändrad 2017-02-22.
CPL Pubid: 243540


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