  A general framework of least squares support vector machine with low rank
kernels, referred to as LR-LSSVM, is introduced in this paper. The special
structure of low rank kernels with a controlled model size brings sparsity as
well as computational efficiency to the proposed model. Meanwhile, a two-step
optimization algorithm with three different criteria is proposed and various
experiments are carried out using the example of the so-call robust RBF kernel
to validate the model. The experiment results show that the performance of the
proposed algorithm is comparable or superior to several existing kernel
machines.
