Abstract
Support Vector Data Description (SVDD) has a limitation for dealing with a large data set in which computational load drastically increases as training data size becomes large. To handle this problem, we propose a new fast SVDD method using K-means clustering method. Our method uses divide-and-conquer strategy; trains each decomposed sub-problems to get support vectors and retrains with the support vectors to find a global data description of a whole target class. The proposed method has a similar result to the original SVDD and reduces computational cost. Through experiments, we show efficiency of our method.
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Kim, P.J., Chang, H.J., Song, D.S., Choi, J.Y. (2007). Fast Support Vector Data Description Using K-Means Clustering. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_64
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DOI: https://doi.org/10.1007/978-3-540-72395-0_64
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