Minimum Distribution Support Vector Clustering

Support vector clustering (SVC) is a boundary-based algorithm, which has several advantages over other clustering methods, including identifying clusters of arbitrary shapes and numbers. Leveraged by the high generalization ability of the large margin distribution machine (LDM) and the optimal margi...

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Autores principales:	Yan Wang, Jiali Chen, Xuping Xie, Sen Yang, Wei Pang, Lan Huang, Shuangquan Zhang, Shishun Zhao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	support vector clustering margin theory mean variance dual coordinate descent Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/df23ca4d08d741968b7d18f15bc25797
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id	oai:doaj.org-article:df23ca4d08d741968b7d18f15bc25797
record_format	dspace
spelling	oai:doaj.org-article:df23ca4d08d741968b7d18f15bc257972021-11-25T17:29:56ZMinimum Distribution Support Vector Clustering10.3390/e231114731099-4300https://doaj.org/article/df23ca4d08d741968b7d18f15bc257972021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1473https://doaj.org/toc/1099-4300Support vector clustering (SVC) is a boundary-based algorithm, which has several advantages over other clustering methods, including identifying clusters of arbitrary shapes and numbers. Leveraged by the high generalization ability of the large margin distribution machine (LDM) and the optimal margin distribution clustering (ODMC), we propose a new clustering method: minimum distribution for support vector clustering (MDSVC), for improving the robustness of boundary point recognition, which characterizes the optimal hypersphere by the first-order and second-order statistics and tries to minimize the mean and variance simultaneously. In addition, we further prove, theoretically, that our algorithm can obtain better generalization performance. Some instructive insights for adjusting the number of support vector points are gained. For the optimization problem of MDSVC, we propose a double coordinate descent algorithm for small and medium samples. The experimental results on both artificial and real datasets indicate that our MDSVC has a significant improvement in generalization performance compared to SVC.Yan WangJiali ChenXuping XieSen YangWei PangLan HuangShuangquan ZhangShishun ZhaoMDPI AGarticlesupport vector clusteringmargin theorymeanvariancedual coordinate descentScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1473, p 1473 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	support vector clustering margin theory mean variance dual coordinate descent Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	support vector clustering margin theory mean variance dual coordinate descent Science Q Astrophysics QB460-466 Physics QC1-999 Yan Wang Jiali Chen Xuping Xie Sen Yang Wei Pang Lan Huang Shuangquan Zhang Shishun Zhao Minimum Distribution Support Vector Clustering
description	Support vector clustering (SVC) is a boundary-based algorithm, which has several advantages over other clustering methods, including identifying clusters of arbitrary shapes and numbers. Leveraged by the high generalization ability of the large margin distribution machine (LDM) and the optimal margin distribution clustering (ODMC), we propose a new clustering method: minimum distribution for support vector clustering (MDSVC), for improving the robustness of boundary point recognition, which characterizes the optimal hypersphere by the first-order and second-order statistics and tries to minimize the mean and variance simultaneously. In addition, we further prove, theoretically, that our algorithm can obtain better generalization performance. Some instructive insights for adjusting the number of support vector points are gained. For the optimization problem of MDSVC, we propose a double coordinate descent algorithm for small and medium samples. The experimental results on both artificial and real datasets indicate that our MDSVC has a significant improvement in generalization performance compared to SVC.
format	article
author	Yan Wang Jiali Chen Xuping Xie Sen Yang Wei Pang Lan Huang Shuangquan Zhang Shishun Zhao
author_facet	Yan Wang Jiali Chen Xuping Xie Sen Yang Wei Pang Lan Huang Shuangquan Zhang Shishun Zhao
author_sort	Yan Wang
title	Minimum Distribution Support Vector Clustering
title_short	Minimum Distribution Support Vector Clustering
title_full	Minimum Distribution Support Vector Clustering
title_fullStr	Minimum Distribution Support Vector Clustering
title_full_unstemmed	Minimum Distribution Support Vector Clustering
title_sort	minimum distribution support vector clustering
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/df23ca4d08d741968b7d18f15bc25797
work_keys_str_mv	AT yanwang minimumdistributionsupportvectorclustering AT jialichen minimumdistributionsupportvectorclustering AT xupingxie minimumdistributionsupportvectorclustering AT senyang minimumdistributionsupportvectorclustering AT weipang minimumdistributionsupportvectorclustering AT lanhuang minimumdistributionsupportvectorclustering AT shuangquanzhang minimumdistributionsupportvectorclustering AT shishunzhao minimumdistributionsupportvectorclustering
_version_	1718412317218045952

Minimum Distribution Support Vector Clustering

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