Improved convex incremental extreme learning machine based on ridgelet and PSO algorithm
Pakarat Musikawan, Khamron Sunat, Sirapat Chiewchanwattana, Punyaphol Horata, Yanika Kongsorot · 2016
The most difficult problem with the extreme learning machine is the selection of the hidden nodes size. The proper number of hidden nodes is predefined through a trial and error approach. The convex incremental extreme learning machine (CI-ELM) has been proposed to tackle this problem. CI-ELM is an incremental constructive neural network with universal approximation abilities. However, we have found that some hidden nodes added into a hidden layer, may play a minor role in the network, which results in an increase in network complexity. In order to avoid this shortcoming, we propose here in an improved convex incremental extreme learning machine with optimal ridgelet hidden nodes (ICOR-ELM). The proposed method uses the ridgelet function as the activation function within the hidden layer. In each step of the learning process, the optimal hidden node parameters, which are optimized through particle swarm optimization (PSO), are added to the existing hidden layer. Experimental results prove that the proposed method can achieve greater generalization performance with more compact architecture than other methods, and demonstrates faster convergence than other incremental ELM methods.