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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...
A feature extraction technique called Successively Orthogonal Discriminant Analysis (SODA) has been recently proposed to overcome the limitation of Linear Discriminant Analysis (LDA), whose objective is to find a projection vector such that the projected values of data from both classes have maximum class separability. However, in LDA, only one such vector can be found due to the rank deficiency for...
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