A Fast Two-Stage Extreme Learning Machine

In order to realize the automatic adjustment of the network structure of the extreme learning machine (ELM), inspired by the two-stage extreme learning machine (TS-ELM), a fast two-stage extreme learning machine (FTS-ELM) is proposed by making the nodes added follow the arithmetic progression and using the principal component analysis (PCA) for pruning the redundant nodes. In the growing stage of hidden nodes, the nodes are added into network according to the arithmetic progression to reduce the number of iterations. In the pruning phase, PCA is used to delete redundant nodes. The hidden nodes with low contribution rate are quickly reduced by continuously reducing the cumulative contribution rate threshold, until the error (accuracy) achieves its maximum (minimum), which makes the network structure more compact. The empirical studies show that compared with ELM, EM-ELM, OP-ELM and TS-ELM algorithms, FTS-ELM leads to a compact network structure with good generalization performance, and its training time is far less than TS-ELM.