Infinite Kernel Extreme Learning Machine

This paper addresses the analysis of the problem of combining Infinite Kernel Learning (IKL) approach and Extreme Learning Machine (ELM) structure. ELM represents a novel and promising alternative to Neural Networks, for its simplicity in implementation and high efficiency, especially concerning convergence and generalization performance. A currently underdeveloped topic concerning ELM implementation is given by the optimization process of base kernels: choosing different kernel combinations may lead to very dissimilar performance results. An innovative ELM approach using a combination of multiple kernels has been proposed in Liu et al. As a change of paradigm, we are interested in using an infinite set of base kernels, defining in this way an original ELM based algorithm called Infinite Kernel Extreme Learning Machine (IK-ELM). About that, a novel 3-step algorithm combining IKL and ELM is proposed. Finally, a brief analysis about further possible directions is discussed.