Variant methods of reduced set selection for reduced support vector machines
Li-Jen Chien, Chienchung Chang, Yuh‐Jye Lee · 2010
In dealing with large datasets the reduced support vector machine (RSVM) was proposed for the practical objective to overcome the computational difficulties as well as to reduce the model complexity. In this paper, we propose two new approaches to gener-ate representative reduced set for RSVM. First, we introduce Clustering Reduced Support Vector Machine (CRSVM) that builds the model of RSVM via RBF (Gaussian kernel) construction. Applying clustering algorithm to each class, we can generate cluster cen-troids of each class and use them to form the reduced set which is used in RSVM. We also estimate the approximate density for each cluster to get the parameter used in Gaus-sian kernel which will save a lot of tuning time. Secondly, we present Systematic Sam-pling RSVM (SSRSVM) that incrementally selects the informative data points to form the reduced set while the RSVM used random selection scheme. SSRSVM starts with an extremely small initial reduced set and adds a portion of misclassified points into the re-duced set iteratively based on the current classifier until the validation set correctness is large enough. We also show our methods, CRSVM and SSRSVM with smaller size of reduced set, have superior performance than the original random selection scheme.