Mathematical Problems in Engineering

Extreme Learning Machine on High Dimensional and Large Data Applications


Status
Published

Lead Editor

1Nanyang Technological University, Nanyang Ave, Singapore

2Hangzhou Dianzi University, Hangzhou, China

3Institute for Infocomm Research (I²R), Singapore

4Beijing Jiaotong University, Beijing, China

5Anhui University, Anhui, China

6Aalto University, Espoo, Finland


Extreme Learning Machine on High Dimensional and Large Data Applications

Description

Extreme learning machine (ELM) is a recently developed training algorithm for single hidden layer feedforward neural networks (SLFNs). ELM theory claims that the hidden node parameters of SLFNs can be randomly generated and need not to be updated. All the hidden node parameters are independent of the target functions or the training datasets. Benefitting from the tuning-free framework, ELM not only learns up to thousands times faster than conventional gradient descent methods for SLFNs and the support vector machine (SVM) but also preserves a reasonable generalization performance. For most applications, it has been shown that the learning phase of ELM can be finished in less than one second in an ordinary PC. Therefore, ELM shows superiority over conventional gradient based methods and SVM on high dimensional applications and large data processing. This is especially important since nowadays data are explosive with the rapid development of the internet, computer, and electronic equipment.

With this objective, we initiate a special issue on ELM and its applications in high dimensional and large data problems. The aim of this special issue is to bring together the latest and innovative developments in theories and algorithms based on ELM for large data applications, such as image processing, video processing, bioinformatics applications, and human-computer interaction. All the submissions are expected to have original ideas and/or new approaches. All the submissions must be related to the ELM technique.

Potential topics include, but are not limited to:

  • Real-time learning for large data applications
  • ELM theory
  • Convergence analysis of ELM algorithms
  • Time-series prediction for large data applications
  • ELM on image and video processing
  • Bioinformatics and biometrics applications with ELM
  • Human-computer interaction with ELM
  • Web applications

Articles

  • Special Issue
  • - Volume 2015
  • - Article ID 624903
  • - Editorial

Extreme Learning Machine on High Dimensional and Large Data Applications

Zhiping Lin | Jiuwen Cao | ... | Amaury Lendasse
  • Special Issue
  • - Volume 2015
  • - Article ID 103796
  • - Review Article

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

Jiuwen Cao | Zhiping Lin
  • Special Issue
  • - Volume 2015
  • - Article ID 317142
  • - Research Article

Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

Pak Kin Wong | Hang Cheong Wong | ... | Xianghui Gao
  • Special Issue
  • - Volume 2015
  • - Article ID 528190
  • - Research Article

Research on Three-dimensional Motion History Image Model and Extreme Learning Machine for Human Body Movement Trajectory Recognition

Zheng Chang | Xiaojuan Ban | ... | Jing Guo
  • Special Issue
  • - Volume 2015
  • - Article ID 484093
  • - Research Article

Online Sequential Prediction for Nonstationary Time Series with New Weight-Setting Strategy Using Extreme Learning Machine

Wentao Mao | Jinwan Wang | ... | Mei Tian
  • Special Issue
  • - Volume 2015
  • - Article ID 236084
  • - Research Article

Efficient ELM-Based Two Stages Query Processing Optimization for Big Data

Linlin Ding | Yu Liu | ... | Junchang Xin
  • Special Issue
  • - Volume 2015
  • - Article ID 790412
  • - Research Article

Daily Human Physical Activity Recognition Based on Kernel Discriminant Analysis and Extreme Learning Machine

Wendong Xiao | Yingjie Lu
  • Special Issue
  • - Volume 2015
  • - Article ID 793697
  • - Research Article

Detecting Copy Directions among Programs Using Extreme Learning Machines

Bin Wang | Xiaochun Yang | Guoren Wang
  • Special Issue
  • - Volume 2015
  • - Article ID 923097
  • - Research Article

Distributed Learning over Massive XML Documents in ELM Feature Space

Xin Bi | Xiangguo Zhao | ... | Shuang Chen
  • Special Issue
  • - Volume 2015
  • - Article ID 326160
  • - Research Article

Data-Driven Dynamic Modeling for Prediction of Molten Iron Silicon Content Using ELM with Self-Feedback

Ping Zhou | Meng Yuan | ... | Tianyou Chai
Mathematical Problems in Engineering
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Acceptance rate27%
Submission to final decision64 days
Acceptance to publication34 days
CiteScore1.800
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