Active set strategy of optimized extreme learning machine

Abstract

Extreme learning machine (ELM) has been introduced as a simple and efficient learning approach for regression and classification applications. From the optimization point of view, optimized ELM is equivalent to SVM, but with less constraints in the optimization formulation and random ELM kernel. This paper introduces an active set based optimized ELM approach to solve bound constrained optimization problem in a straightforward way, which operates on a small working set of variables at each iteration. Thus, the constrained problem can be eventually solved by an unconstrained algorithm, and this enables us to establish a global convergence theory. The approach requires less time for quadratic programming solving and provides better generalization performance. In addition, the proposed approach with much smaller number of non-bound support values is significantly faster than SVM with active set strategy for large training data set.