Canonical ELM: Improving the Performance of Extreme Learning Machines on Multivariate Regression Tasks Using Canonical Correlations

Abstract

The extreme learning machine (ELM), an algorithm for training feedforward neural networks, is described in the literature as an algorithm that is suitable for both multiclass classification and multivariate regression problems. In this paper, we show that the closed-form ELM solution is not optimal for multivariate regression problems because it ignores correlations between the different response or target variables. We propose an improved algorithm, the canonical ELM, that accounts for the correlations between the target variables, and yet adheres to the ELM principle of learning without iteratively updating the weights in the network. Experimental results obtained using a synthetic dataset and several real-world datasets show that the canonical ELM has a higher prediction accuracy than the ELM and is also more stable.