Constrained Extreme Learning Machine: A novel highly discriminative random feedforward neural network

Abstract

In this paper, a novel single hidden layer feedforward neural network, called Constrained Extreme Learning Machine (CELM), is proposed based on Extreme Learning Machine (ELM). In CELM, the connection weights between the input layer and hidden neurons are randomly drawn from a constrained set of difference vectors of between-class samples, rather than an open set of arbitrary vectors. Therefore, the CELM is expected to be more suitable for discriminative tasks, whilst retaining other advantages of ELM. The experimental results are presented to show the high efficiency of the CELM, compared with ELM and some other related learning machines.