Improved Extreme Learning Machine Based on Deep Learning and Its Application in Handwritten Digits Recognition

Abstract

Traditional extreme learning machine (ELM) requires a large number of hidden layer neurons in its applications, and the ability to process high-dimensional big data samples is weak. In response to the above problems, this paper proposes an improved extreme learning machine algorithm based on deep learning. This algorithm combines the double pseudo-inverse extreme learning machine (DPELM) algorithm, which has high classification accuracy and simple network structure, with the denoising autoencoder (DAE) which can extract more essential data features. Among them, DAE is used to extract the features of the data that needs to be recognized, and the DPELM mainly plays as a classifier to quickly classify and recognize the extracted features. Experimental results show that in the recognition of handwritten digits, the double pseudo-inverse extreme learning machine based on denoising autoencoder (DAE-DPELM) algorithm needs only a small number of hidden layer neurons. In addition, compared with the traditional ELM algorithm and DAE-ELM algorithm, DAE-DPELM algorithm has a higher classification accuracy.