Large-Scale Kernel Extreme Learning Machine
Deng Wan · Chinese Journal of Computers · 2014
Kernel Extreme Learning Machine(KELM)generalizes basic Extreme Learning Machine(ELM)to the kernel-based framework,and produces better generalization than ELM.But its time O(n2 m+n3+ns)≈O(n3)(where n is the number of training sets,mis the number of dimensions and s is the number of output nodes)increases polynomial with respect to the data size,and thus unsuitable for large-scale problems(n=20 000).Here we will propose an accelerated framework for KELM,and then implement an effective algorithm named Nystrm Kernel Extreme Learning Machine(NKELM)based on Nystrm low-rank decomposition under the framework.The time cost of NKELM O(nmL+mL2+L3+nLs)≈O(n)(Lis the number of hidden nodes,and Lnin common cases)is significantly lower than KELM,and very suitable for large-scale problems.The experimental results on large-scale datasets show that NKELM can produce good generalization performance with fast learning speed.