MULTI-KERNEL SUPPORT VECTOR CLUSTERING FOR MULTI-CLASS CLASSIFICATION

Applying Support vector clustering (SVC) to multi-class classification problems has difficulty in determining the hyperparameters of the kernel functions. Multi-kernel learning has been proposed to overcome this difficulty, by which kernel matrix weights and Lagrange multipliers can be simultaneously derived with semidefinite programming. However, the amount of time and space required is very demanding. We develop a two-stage multi-kernel learning algorithm which conducts sequential minimal optimization and gradient projection iteratively. One multi-kernel SVC is constructed for the patterns of each class. The outputs obtained by all the multi-kernel SVCs are integrated and a discriminant function is applied to make the final multi-class decision. Experimental results on data sets taken from UCI and Statlog show that the proposed approach performs better than other methods.