Local Support Vector Machine based on Cooperative Clustering for very large-scale dataset

Local support vector machine (LSVM) has been attracting more and more attention because of its consistency. In LSVM, the training of a standard SVM is transformed to the construction of a set of local model of SVM, each of which is obtained by the training on the neighborhood of a certain sample. This strategy reduces the number of samples in every turn of training for the construction of SVM, but increased the number of local model to be trained. Some methods had been proposed to reduce the number of local model which is needed to be trained. However, theses reduction is not enough for very large-scale dataset. In this paper, we present a new Local Support Vector Machine algorithm based on Cooperative Clustering, namely C²LSVM and do the description of the C²LSVM algorithm and experiment In C²LSVM, the data of training subset will be reduced from thousands down to tens. At the same time, the classification accuracy will be preserved even improved.