A hierarchical structure of extreme learning machine (HELM) for high-dimensional datasets with noise

2014 ◽  
Vol 128 ◽  
pp. 407-414 ◽  
Author(s):  
Yan-Lin He ◽  
Zhi-Qiang Geng ◽  
Yuan Xu ◽  
Qun-Xiong Zhu
Keyword(s):  
Extreme Learning Machine ◽  
Hierarchical Structure ◽  
High Dimensional ◽  
Learning Machine ◽  
High Dimensional Datasets

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

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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