Proceedings

Data Sparseness in Linear SVM

Xiang Li, Huaimin Wang, Bin Gu, Charles X. Ling

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Machine Learning. Pages 3628-3633.

Abstract

Large sparse datasets are common in many real-world applications. Linear SVM has been shown to be very efficient for classifying such datasets. However, it is still unknown how data sparseness would affect its convergence behavior. To study this problem in a systematic manner, we propose a novel approach to generate large and sparse data from real-world datasets, using statistical inference and the data sampling process in the PAC framework. We first study the convergence behavior of linear SVM experimentally, and make several observations, useful for real-world applications. We then offer theoretical proofs for our observations by studying the Bayes risk and PAC bound. Our experiment and theoretic results are valuable for learning large sparse datasets with linear SVM.

Xiang Li, Huaimin Wang, Bin Gu, and Charles X. Ling. 2015. Data Sparseness in Linear SVM. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 3628–3633. https://ijcai.org/proceedings/2015/510
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