Abstract
Extreme learning machine (ELM) solves regression and classification problems efficiently. However, the solution provided is dense and requires plenty of storage space and testing time. A sparse ELM has been proposed for classification in [1]. However, it is not applicable for regression problems. In this paper, we propose a sparse ELM for regression, which significantly reduces the storage space and testing time. In addition, we develop an efficient training algorithm based on iterative computation, which scales quadratically with regard to the number of training samples. Therefore, the proposed sparse ELM is advantageous over other ELM methods when facing large data sets for achieving faster training and testing speed, while requiring less storage space. In addition, sparse ELM outperforms support vector regression (SVR) in the aspects of generalization performance, training speed and testing speed.
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