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Entropy 2018, 20(9), 674; https://doi.org/10.3390/e20090674

Study on Mutual Information and Fractal Dimension-Based Unsupervised Feature Parameters Selection: Application in UAVs

1
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China
2
Institute of Unmanned System, Beihang University, Beijing 100191, China
3
Key Laboratory of Advanced Technology of Intelligent Unmanned Flight System of Ministry of Industry and Information Technology, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Received: 16 July 2018 / Revised: 2 September 2018 / Accepted: 3 September 2018 / Published: 5 September 2018
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Abstract

In this study, due to the redundant and irrelevant features contained in the multi-dimensional feature parameter set, the information fusion performance of the subspace learning algorithm was reduced. To solve the above problem, a mutual information (MI) and fractal dimension-based unsupervised feature parameters selection method was proposed. The key to this method was the importance ordering algorithm based on the comprehensive consideration of the relevance and redundancy of features, and then the method of fractal dimension-based feature parameter subset evaluation criterion was adopted to obtain the optimal feature parameter subset. To verify the validity of the proposed method, a brushless direct current (DC) motor performance degradation test was designed. Vibrational sample data during motor performance degradation was used as the data source, and motor health-fault diagnosis capacity and motor state prediction effect ware evaluation indexes to compare the information fusion performance of the subspace learning algorithm before and after the use of the proposed method. According to the comparison result, the proposed method is able to eliminate highly-redundant parameters that are less correlated to feature parameters, thereby enhancing the information fusion performance of the subspace learning algorithm. View Full-Text
Keywords: unsupervised feature selection; feature extraction; mutual information; fractal dimension; subspace learning algorithm unsupervised feature selection; feature extraction; mutual information; fractal dimension; subspace learning algorithm
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Wang, X.; He, Y.; Wang, L. Study on Mutual Information and Fractal Dimension-Based Unsupervised Feature Parameters Selection: Application in UAVs. Entropy 2018, 20, 674.

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