Multiple Kernel Sphere with Large Margin for Novelty Detection
نویسندگان
چکیده
Novelty detection methods have been frequently applied in medical diagnosis, fault detection, network security and the discovery of new species. Among them, Support Vector Data Description (SVDD) has received considerable attention for its comprehensivedescription ability which covers the target data. Additionally, the Multiple Kernel Learning (MKL) technique has been extensively applied in machine learning methods; e.g. the SVM classifiers, dimensionality reduction techniques, etc. In this paper, we focus on the application of the MKL method on novelty detection (ND) and propose the new method of Multiple Kernel Sphere with Larger Margin (MKSLM) for novelty detection. In the presented method, the volume of the sphere is minimized while the margin between the surface of the sphere and the outliers are maximized to obtain a sphere with minimum size. An algorithm is also developed to solve the optimization problem. Experimental results over various real data sets have validated the superiority of the proposed methods
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