scholarly journals Extreme Learning Machine on High Dimensional and Large Data Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-2 ◽  
Author(s):  
Zhiping Lin ◽  
Jiuwen Cao ◽  
Tao Chen ◽  
Yi Jin ◽  
Zhan-Li Sun ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Large Data ◽  
High Dimensional ◽  
Learning Machine

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

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jiuwen Cao ◽  
Zhiping Lin
Keyword(s):  
Linear System ◽  
Video Processing ◽  
Large Data ◽  
Feedforward Neural Networks ◽  
High Dimensional ◽  
Recent Developments ◽  
Learning Machine ◽  
Hidden Layer ◽  
Hidden Neurons ◽  
The Cost

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.

Two-stage extreme learning machine for high-dimensional data

2014 ◽  
Vol 7 (5) ◽  
pp. 765-772 ◽  
Author(s):  
Peng Liu ◽  
Yihua Huang ◽  
Lei Meng ◽  
Siyuan Gong ◽  
Guopeng Zhang
Keyword(s):  
Extreme Learning Machine ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Two Stage ◽  
Learning Machine

Extreme Learning Machine with Harmony Search for High Dimensional Data Classifications

2021 ◽  
Author(s):  
Sicheng Li ◽  
Zhihong Man ◽  
Zhenwei Cao ◽  
Jinchuan Zheng ◽  
Yuan Chen
Keyword(s):  
Extreme Learning Machine ◽  
High Dimensional Data ◽  
Harmony Search ◽  
High Dimensional ◽  
Learning Machine

Recognition Model Based Feature Extraction and Kernel Extreme Learning Machine for High Dimensional Data

2014 ◽  
Vol 875-877 ◽  
pp. 2020-2024 ◽  
Author(s):  
Yan Shi ◽  
Li Jie Zhao ◽  
Jian Tang
Keyword(s):  
Feature Extraction ◽  
Extreme Learning Machine ◽  
Near Infrared ◽  
High Dimensional Data ◽  
Disease Diagnosis ◽  
Mass Spectrometric ◽  
High Dimensional ◽  
Learning Speed ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

High dimensional data such as mass-spectrometric and near-infrared spectrum are always used in disease diagnosis and product quality monitoring. Aim at the nonlinear feature extraction and low learning speed problems, a novel modeling approach combined principal component analysis (PCA) with kernel extreme learning machine (KELM) is proposed. The extracted features using PCA algorithms are fed into nonlinear classification based KELM with fast learning speed. The numbers of the features are selected according the classification performance. The experimental results based on the mass-spectrometric data in the benchmark demonstrate that the proposed approach has better performance. This approach can also be used to target recognition based on radar data.

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