Research And Application On The Multi-classification Of Relevance Vector Machine Algorithm

Relevance vector machine (RVM) technique is a novel pattern recognition method ofsupervision machine learning which is based on Bayesian learning theory. It was developedon the basis of Support vector machine(SVM) learning theory, compared with the SVM, it hasthe benefits of sparser model、the facility to utilize arbitrary kernel functions、more accurate、strong robusticity、 intensity generalization ability， and so on. RVM algorithm has beendeveloped rapidly in the fields of application, and proved better in pattern classification、faultdiagnosis、intelligent forecast、information processing of voice and image, etc. Whereas，tosolve the questions of multi-mode pattern recognition，RVM algorithm still suffer from theproblems of looking after both sides of accuracy and real-time because of the complexity ofcomputational process.According to the insufficiency of RVM, the structure and key steps of the algorithm,including kernel functions selection, classifier construct, and control parameter setting, aredeeply investigated in this paper and improved on “one against one” classifier which has beenthe highest accuracy and broad application are proposed to improve. The improvedclassification method proposed is utilized successfully to reduce the time of classification andincrease in real-time without cutting down the accuracy. In addition, improved algorithm inthis paper is applied in face recognition&automobile engine fault diagnosis, and behavedwell.Firstly, the research situation and the fundamental theory about RVM is detail discussedin this paper, and the solved key problem of RVM is presented. In order to improveclassification speed of multiclass pattern recognition based on relevance vector machinelearning algorithm， investigated the method of relevance vector machine algorithm inmulti-mode classification, and found that the comparison too many times was the main reasonfor large amount of calculation. Proposed a new waythat eliminated the most dissimilar classin each round of comparison. Comparison times were reduced step by step per cycle, and theclassification was more, the decrease in the total calculation amount was more obvious. Thevalidity of this method has been proved by some simulated examples, and the experimentalresults of data classification show that compared with traditional classifier, the training timesand the recognition times of the novel method are greatly reduced under the premise of hardly influence classification accuracy, the algorithm running speed is improved obviously.Secondly, to solve the problems existed in face recognition, such as lack of accuracy、real-time and stability，a new face recognition approach based on improved relevance vectormachine is presented in this paper. Firstly, the wavelet transform is applied to preprocess faceimage to reduce the impact of expression change. Then, in order to extract key features of theprocessed face image, use the principal component analysis (PCA) method. Finally, the RVMclassification model is adopted for identifying. In comparison with the support vectormachine(SVM) method, the RVM approach performs well and can obtain more satisfactoryresults in terms of recognition rates、real-time and reliability.Thirdly, the face images recognition accuracy will be obvious decline when the objectscontain more noise. At present, face recognition technology to solve this problem is no betterway. In this paper, a new method of face recognition based on relevance vector machine waspresented. After the wavelet decomposition and PCA transform, relevance vectors fromsample training constituted a "hyperplane" as the differences in the classification of thesamples by machine learning algorithm and used the improved “one against one” method toachieve multi-class pattern recognition. Compared with the former method, a large number ofsimulation results show that the new method used in noisy objects being recognized is notsensitive to image noise, with a more accurate and strong robusticity. In addition, photo light、angle change、occlusion、low resolution ratio and so on, also be discussed and analysed inexperiment with new method in this paper.Finally, application of RVM in the automobile engine fault diagnosis is investigated.From the study we know that the parameter of penalty factor and kernel paraeter play a veryimportant roal on the diagnosis model, so the Particle Swarm Optimization(PSO) is used tooptimize the parameters, this algorithms practical applied to automotive engine fault diagnosis.Considering the problem of the variation of characteristic parameters are followed by enginerotational speed, puts forward a adaptive fitting of super-parameters on incremental learning.To the problem of engine misfire, mapping relations established between gas volume fractionand the cause of the misfire, used normalized data with different gears in machine training,ajusted super-parameters by curve fitting, and the trained RVM model applied in faultclassification and diagnosis.The simulation experiments shows the results of new method isnot only accurate and reliable but also resolve the problem of dynamic detection with variable speed in traditional methods.In conclusion, a simple summary is made and some research aspects are presented in thefuture.