Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey

Jiuwen Cao,Zhiping Lin

doi:10.1155/2015/103796

Abstract

Extreme learning machine (ELM) has been developed for single hidden layer feedforward neural networks (SLFNs). In ELM algorithm, the connections between the input layer and the hidden neurons are randomly assigned and remain unchanged during the learning process. The output connections are then tuned via minimizing the cost function through a linear system. The computational burden of ELM has been significantly reduced as the only cost is solving a linear system. The low computational complexity attracted a great deal of attention from the research community, especially for high dimensional and large data applications. This paper provides an up-to-date survey on the recent developments of ELM and its applications in high dimensional and large data. Comprehensive reviews on image processing, video processing, medical signal processing, and other popular large data applications with ELM are presented in the paper.

Highlights

It is well known that the back-propagation (BP) based algorithms [1,2,3] played dominant roles in training feedforward neural networks (FNNs) in the past several decades
This paper presented an up-to-date review on the recent developments of Extreme learning machine (ELM) algorithms and its applications for high dimensional and large data processing
As presented in the above section, the fast data learning speed and easy implementation characteristics of ELM boosted its applications in various fields

Summary

Introduction

It is well known that the back-propagation (BP) based algorithms [1,2,3] played dominant roles in training feedforward neural networks (FNNs) in the past several decades. Many efforts have been paid to enhance the BP algorithm, challenging issues such as local minima, timecosting in learning, and manual parameter setups still remain in the training phase and are not well addressed in the literature These drawbacks may limit its applications in high dimensional and large data. Comparing to artificial neural networks, the relatively high generalization performance of SVM attracted increasing attention from researchers and engineers in the past two decades. As the main concerns in [104, 105] are on the survey of theory and algorithm developments for ELM and its possible connections with human brain biological learning mechanisms, this paper aims to provide a detailed review on high dimensional and large data applications with ELM and its variants. Conclusions and future perspectives are provided in the paper

ELM Algorithms

ELM in High Dimensional and Large Data Applications

Conclusions