A Novel Meta-ELM with Orthogonal Constraints for Regression Problems

ELM is an innovative learning algorithm that minimizes output error by only finding optimal output weights. Meta-learning is composed of base ELMs and exhibits good generalization. To improve its performance further by introducing orthogonal constraints into the base ELMs and “top” ELM, we propose a novel Meta-ELM with orthogonal constraints (Meta-QEC-ELM). Because of the particularity of the Meta-ELM, its orthogonal constraint problem is the quadratic equality constraint problem—that is, a one-column Procrustes problem—and it can preserve much more information from feature space to output subspace. The experimental results show that the Meta-QEC-ELM is both effective and feasible.