Abstract
This paper presents a new algorithm named Kernel Bisecting k-means and Sample Removal (KBK-SR) as a sampling preprocess for SVM training to improve the scalability. The novel top-down clustering approach Kernel Bisecting k-means in the KBK-SR tends to fast produce balanced clusters of similar sizes in the kernel feature space, which makes KBK-SR efficient and effective for reducing training samples for nonlinear SVMs. Theoretical analysis and experimental results on three UCI real data benchmarks both show that, with very short sampling time, our algorithm dramatically accelerates SVM training while maintaining high test accuracy.
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Wang, TB. (2018). A Novel Kernel Clustering Method for SVM Training Sample Reduction. In: Hu, T., Wang, F., Li, H., Wang, Q. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11338. Springer, Cham. https://doi.org/10.1007/978-3-030-05234-8_7
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DOI: https://doi.org/10.1007/978-3-030-05234-8_7
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