A novel clustering method for complex signals and feature extraction based on advanced information-based dissimilarity measure
In this paper, a new dissimilarity measure for more accurate feature extraction and clustering is put forward. The method is proposed from the perspective of the weighted-probability distribution of dispersion patterns and their rank order statistics, where an effective quantization procedure is pro...
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| Published in | Expert systems with applications Vol. 238; p. 122011 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
15.03.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2023.122011 |
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| Abstract | In this paper, a new dissimilarity measure for more accurate feature extraction and clustering is put forward. The method is proposed from the perspective of the weighted-probability distribution of dispersion patterns and their rank order statistics, where an effective quantization procedure is provided and the loss of information can be reduced. The proposed dissimilarity is applied in the multidimensional scaling (MDS) method to investigate simulated and reality-based signals. The comparative experiment shows that the clustering results of the proposed technique are clearer and more appropriate. State-of-the-art techniques and conventional methods are both included in the comparative experiments. In particular, for the heartbeat signals, it is discovered that the distribution of the weighted-probabilities of the dispersion patterns can discriminate subjects with different physiological conditions and exhibit visible changes of the subject’s dynamical features when aging and disease attacks are taken place, which can be regarded as a microscopic insight of the dynamical mechanisms. |
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| AbstractList | In this paper, a new dissimilarity measure for more accurate feature extraction and clustering is put forward. The method is proposed from the perspective of the weighted-probability distribution of dispersion patterns and their rank order statistics, where an effective quantization procedure is provided and the loss of information can be reduced. The proposed dissimilarity is applied in the multidimensional scaling (MDS) method to investigate simulated and reality-based signals. The comparative experiment shows that the clustering results of the proposed technique are clearer and more appropriate. State-of-the-art techniques and conventional methods are both included in the comparative experiments. In particular, for the heartbeat signals, it is discovered that the distribution of the weighted-probabilities of the dispersion patterns can discriminate subjects with different physiological conditions and exhibit visible changes of the subject’s dynamical features when aging and disease attacks are taken place, which can be regarded as a microscopic insight of the dynamical mechanisms. |
| ArticleNumber | 122011 |
| Author | Shang, Du Shang, Pengjian Li, Ang |
| Author_xml | – sequence: 1 givenname: Du surname: Shang fullname: Shang, Du email: 19118017@bjtu.edu.cn organization: State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing 100044, China – sequence: 2 givenname: Pengjian surname: Shang fullname: Shang, Pengjian email: pjshang@bjtu.edu.cn organization: School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, PR China – sequence: 3 givenname: Ang surname: Li fullname: Li, Ang email: 22110496@bjtu.edu.cn organization: School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, PR China |
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| Keywords | Dispersion patterns Multi- dimensional scaling Weighted-probability distribution Complex signals Clustering Dissimilarity |
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