Sparse Least Squares Low Rank Kernel Machines
This is an incremental improvement for kernel machine applications, offering more efficient computation.
The authors introduced LR-LSSVM, a least squares support vector machine framework using low-rank kernels to achieve sparsity and computational efficiency, with experiments showing it performs comparably or better than existing kernel machines.
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.