A novel orthogonal extreme learning machine for regression and classification problems

Abstract

An extreme learning machine (ELM) is an innovative algorithm for the single hidden layer feed-forward neural networks and, essentially, only exists to find the optimal output weight so as to minimize output error based on the least squares regression from the hidden layer to the output layer. With a focus on the output weight, we introduce the orthogonal constraint into the output weightmatrix, and propose a novel orthogonal extreme learning machine (NOELM) based on the idea of optimization column by column whosemain characteristic is that the optimization of complex output weightmatrix is decomposed into optimizing the single column vector of the matrix. The complex orthogonal procrustes problem is transformed into simple least squares regression with an orthogonal constraint, which can preserve more information from ELM feature space to output subspace, these make NOELM more regression analysis and discrimination ability. Experiments show that NOELMhas better performance in training time, testing time and accuracy than ELMand OELM.