Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data

2016 ◽  
Vol 9 (2) ◽  
pp. 129-139 ◽  
Author(s):  
Nan Zhang ◽  
Shifei Ding
Keyword(s):  
Extreme Learning Machine ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Learning Machine

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.

scholarly journals Predicting future dynamics from short-term time series using an Anticipated Learning Machine

2020 ◽  
Vol 7 (6) ◽  
pp. 1079-1091 ◽  
Author(s):  
Chuan Chen ◽  
Rui Li ◽  
Lin Shu ◽  
Zhiyu He ◽  
Jining Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
High Dimensional Data ◽  
Small Sample ◽  
High Dimensional ◽  
Target Variable ◽  
Short Term ◽  
Linear Dynamical Systems ◽  
Non Linear ◽  
Learning Machine

Abstract Predicting time series has significant practical applications over different disciplines. Here, we propose an Anticipated Learning Machine (ALM) to achieve precise future-state predictions based on short-term but high-dimensional data. From non-linear dynamical systems theory, we show that ALM can transform recent correlation/spatial information of high-dimensional variables into future dynamical/temporal information of any target variable, thereby overcoming the small-sample problem and achieving multistep-ahead predictions. Since the training samples generated from high-dimensional data also include information of the unknown future values of the target variable, it is called anticipated learning. Extensive experiments on real-world data demonstrate significantly superior performances of ALM over all of the existing 12 methods. In contrast to traditional statistics-based machine learning, ALM is based on non-linear dynamics, thus opening a new way for dynamics-based machine learning.

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