Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

# Feature Selection Based on Robust LLE Vote and Its Application to Bearing Fault Diagnosis

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. Preprints 2019, 2019110261 (doi: 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 (doi: 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.

## Subject Areas

feature selection; locally linear embedding; regularization technology; bearing fault diagnosis

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