A randomized ELM speedup algorithm

Abstract

Extreme learning machine (ELM) as an emergent technology has shown its good performance in classification applications. However, ELM algorithm needs to find the inversion of matrix in nature, which will limit its application on many occasions. This paper proposes an ELM speedup algorithm based on the analysis of ELM algorithm. By applying randomized approximation method, the proposed algorithm can approximate the key matrix (For example, the kernel matrix in the kernel-based ELM) with a low-rank matrix. By doing so, the complexity of the inversion can be reduced from O(n3) to O(kn2+k3) (n is the size of the data set, and k is the numerical rank of the approximated matrix). On the premise of not decreasing the accuracy too much, the training time can be cut down substantially, which has important significance in practical application of machine learning algorithms. The experimental results on benchmark data sets demonstrate the effectiveness of the proposed algorithm.