Unsupervised clustering based reduced support vector machines

To overcome the vast computation of the standard support vector machines (SVMs), Lee and Mangasarian (see First SIAM International Conference on Data Mining, 2001) proposed reduced support vector machines (RSVM). But they select ´support vectors´ randomly from the training set, and this will affect the test result. In this paper, we select some representative vectors as support vectors via a simple unsupervised clustering algorithm, and then apply the RSVM method on these vectors. The proposed method can get higher recognition accuracy with fewer support vectors compared to the original RSVM, with the advantage of reducing the running time significantly.