Research On 1-SVM Based Multi-sphere Classifier And Its Application

As important algorithms inspired by support vector machine (SVM), one-class SVM (1-SVM) and multi-sphere are well applied to novelty detection, clustering and so on. Based on the research on 1-SVM theory, this paper proposes active set method for 1-SVM training, and introduces multi-sphere with fuzzy factors to solve supervised classification problems.This research mainly consists of the following parts:(1) A recursive training method based on active set is proposed to 1-SVM training. It focuses on the optimal distribution of the support vectors rather than the convergence of the objective function, hence the absolutely analytical solution is obtained without the sensitivity of the KKT tolerance.(2) To solve the problem of distance parameters in 1-SVM based clustering, an improved clustering algorithm based on fuzzy 1-SVM (1-FSVM) is proposed. The distance parameters are replaced with SVM featured fuzzy membership functions and the clustering center is prevented from being affected by the abnormal data, hence the robustness of the algorithm against the irregularly distributed data is improved without extra searching of distance parameters. Meanwhile, the framework of multi-sphere is proposed from this kernel based clustering algorithm.(3) The multi-sphere classifier (MSC) is proposed by expending the multi-sphere framework to supervised learning, and the compacted one-vs-rest (1VR) classifier is introduced to separate the mixed samples in the spheres. These two complementary classifiers can be combined into a novel weighted classifier. Cross validation is used here to evaluate the weights and search the optimal training parameters. When compared with the traditional 1VR classifier, this novel classifier gets higher accuracy with less training time and decision time.(4) To decrease the decision time in one-vs-one (1V1) classifier with pairwise coupling (PWC), MSC is used to obtain the fuzzy memberships between the samples and the object classes. Two pre-classification methods are introduced to pick out a part of classes with higher fuzzy memberships for the further PWC decision. The first method fixes the number of the classes which will be involved in the final decision. It makes a trade-off between the decision time and the accuracy by regulating a tolerance parameter. It can save much decision computational costs with a little payment of accuracy. The last method uses K-means to get a clustering with higher fuzzy memberships. Since it considers the difference of the samples, the accuracy remains almost the same.(5) In the license plate recognition project, linear transformations for image are used to get virtual license plate character data. Multi-class classifiers based on multi-sphere proposed in this paper are applied to the on-line character recognition module of the project. By the comparison, the 1V1 classifier with PWC and K-means based pre-classification is selected for the project.