KPCA-based Node Selection for Fast KMSE
نویسندگان
چکیده
In this paper we first show that the kernel minimum squared error model is not computationally efficient in feature extraction. To speed up the feature extraction, we linearly express the feature extractor using nodes, i.e. a portion of the training samples in the kernel space. For node selection from the training set, we define two criteria based on Kernel principal component analysis. The nodes are representative and not similar to each other. The experimental results show the feasibility of the proposed method.
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