Efficient support vector data descriptions for novelty detection
Support vector data description (SVDD) is a very attractive kernel method in many novelty detection problems. However, the decision function of SVDD is expressed in terms of the kernel expansion, which results in a run-time complexity linear in the number of support vectors (SVs). In this paper, an efficient SVDD (E-SVDD) is proposed to improve the prediction speed of SVDD. This proposed E-SVDD first finds some crucial feature vectors by the partitioning-entropy-based kernel fuzzy c-means (KFCM) cluster technique and then uses the images of the preimages of these feature vectors to reexpress the center of SVDD. Hence, the decision function of E-SVDD only contains some crucial kernel terms, and the complexity of E-SVDD is linear in the number of the clusters. The experimental results on several benchmark datasets indicate that E-SVDD not only obtains fast prediction speed but also shows better stable generalization than other methods.
support vector data description novelty detection kernel method preimage partitioning-entropy-based kfcm