Version 1
: Received: 19 November 2019 / Approved: 22 November 2019 / Online: 22 November 2019 (10:05:03 CET)
How to cite:
Yin, H.; Hu, Z.; Liu, Y. Feature Selection Based on Robust LLE Vote and Its Application to Bearing Fault Diagnosis. Preprints2019, 2019110261. https://doi.org/10.20944/preprints201911.0261.v1
Yin, H.; Hu, Z.; Liu, Y. Feature Selection Based on Robust LLE Vote and Its Application to Bearing Fault Diagnosis. Preprints 2019, 2019110261. https://doi.org/10.20944/preprints201911.0261.v1
Yin, H.; Hu, Z.; Liu, Y. Feature Selection Based on Robust LLE Vote and Its Application to Bearing Fault Diagnosis. Preprints2019, 2019110261. https://doi.org/10.20944/preprints201911.0261.v1
APA Style
Yin, H., Hu, Z., & Liu, Y. (2019). Feature Selection Based on Robust LLE Vote and Its Application to Bearing Fault Diagnosis. Preprints. https://doi.org/10.20944/preprints201911.0261.v1
Chicago/Turabian Style
Yin, H., Zebiao Hu and Yuanhong Liu. 2019 "Feature Selection Based on Robust LLE Vote and Its Application to Bearing Fault Diagnosis" Preprints. https://doi.org/10.20944/preprints201911.0261.v1
Abstract
The purpose of feature selection is to find important features from the original high-dimensional space. As atypical feature selection algorithm, Locally linear embedding(LLE)-based feature selection algorithm, which applies the idea of LLE to the graph-preserving feature selection framework, has been received wide attention. However, LLE-based feature selection framework is sensitive to noise and K-nearest neighbors. To address these problems, an improved LLE-based feature selection algorithm, robust LLE (RLLE) vote, is proposed. In this algorithm, $l_1$ and $l_2$ regularization are introduced into the high-dimensional reconstruction model of LLE. Furthermore, RLLE vote also proposes a criterion to measure the difference between the reconstruction features and the original features, and then the importance features can be selected by this criteria. Extensive experiments are carried out on a benchmark fault data set and the bearing data set collected from our own laboratory, and the experimental results demonstrate that RLLE vote achieves the most significant performance compared existing state-of-art methods.
Keywords
feature selection; locally linear embedding; regularization technology; bearing fault diagnosis
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
