An Informative Feature Extraction Algorithm for Kernel Machines
AbstractIn this paper we propose a novel method for feature extraction tasks. The algorithm contains three stages: quantifying of feature difference to determining the importance of all features; constructing a feature extraction model according to the traditional nearest neighbour principle and optimizing this model using gradient based methods. Experimental results on benchmark data set have validated the effectiveness of the proposed method.
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