Scaling up support vector data description by using core-sets

• Published in: 2004 IEEE International Joint Conference on Neural Networks Vol. 1; pp. 425 - 430
• Main Authors: Chu, C.S., Tsang, I.W., Kwok, J.T.
• Format: Conference Proceeding
• Language: English
• Published: Piscataway NJ IEEE 2004
• Subjects: Applied sciences; Artificial intelligence; Computer science; Computer science; control theory; systems; Covariance matrix; Electronic mail; Event detection; Exact sciences and technology; Kernel; Machine learning; Packaging; Quadratic programming; Support vector machine classification; Support vector machines; Computational geometry; Scalability; Data description; Very large databases; Vector support machine; Linear time; Neural network; Quadratic programming; Kernel method
• ISBN: 0780383591; 9780780383593
• ISSN: 1098-7576
• DOI: 10.1109/IJCNN.2004.1379943

Support vector data description (SVDD) is a powerful kernel method that has been commonly used for novelty detection. While its quadratic programming formulation has the important computational advantage of avoiding the problem of local minimum, this has a runtime complexity of O(N/sup 3/), where N is the number of training patterns. It thus becomes prohibitive when the data set is large. Inspired from the use of core-sets in approximating the minimum enclosing ball problem in computational geometry, we propose An approximation method that allows SVDD to scale better to larger data sets. Most importantly, the proposed method has a running time that is only linear in N. Experimental results on two large real-world data sets demonstrate that the proposed method can handle data sets that are much larger than those that can be handled by standard SVDD packages, while its approximate solution still attains equally good, or sometimes even better, novelty detection performance.