Training sparse SVM on the core sets of fitting-planes

Controlling the sparsity of a classifier is a key to train SVM efficiently on very large scale problems. This paper explores building SVM classifier on the fitting-plane of each class of data, which captures the distributing trend of the corresponding class of data. The newly developed plane-fitting model can be solved by core set methods, and the SVM is trained only on the core sets which are small subsets of the original data. The computing complexity of the proposed algorithm is up bounded by Ο(1/ε)Ο(1/ε). Experimental results show that the new algorithm scales better than SVMperf and CVM/BVM, while their predicting accuracies are almost comparable.