Clustering in extreme learning machine feature space

Extreme learning machine (ELM), used for the “generalized” single-hidden-layer feedforward networks (SLFNs), is a unified learning platform that can use a widespread type of feature mappings. In theory, ELM can approximate any target continuous function and classify any disjoint regions; in application, many experiment results have already demonstrated the good performance of ELM. In view of the good properties of the ELM feature mapping, the clustering problem using ELM feature mapping techniques is studied in this paper. Experiments show that the proposed ELM kMeans algorithm and ELM NMF (nonnegative matrix factorization) clustering can get better clustering results than the corresponding Mercer kernel based methods and the traditional algorithms using the original data. Moreover, the proposed methods have the advantage of being more convenient to implementation and computation, as the ELM feature mapping is much simpler than the Mercer kernel function based feature mapping methods.