Multi-Representatives-Based Algorithm for Subspace Classification
Lifei Chen · Jisuanji kexue yu tansuo · 2011
The multi-representatives nearest neighbor classifier,which builds classification model using model clusters centered with representatives,and determines the number of nearest neighbors automatically,has been proposed to overcome the shortcomings of traditional nearest neighbor algorithms.However,it would increase the number of model clusters when the samples in different categories are overlapped,and subsequently the prediction accuracy is affected.This paper proposes a multi-representatives-based algorithm for subspace classification,where the training samples are projected onto some different subspaces in order to construct the classification model consisting of model clusters in individual subspaces.This method makes the overlapped samples belonging to different classes in the entire space easily separable,so that the classification performances can be improved.In comparison with other methods such as traditional kNN(k nearest neighbor),kNNModel,SVM(support vector machine),etc.,the experimental results show that the proposed method significantly improves the accuracy of the classification on datasets with complex category structures.