Improve the spectral clustering by integrating a new modularity similarity index and out-of-sample extension

doi:10.1142/S0217984920501055

CSpace

	Improve the spectral clustering by integrating a new modularity similarity index and out-of-sample extension
	Shen, Dongqin 1; Li, Xiuyi 2; Yan, Guan 3
	2020-04-01
发表期刊	MODERN PHYSICS LETTERS B
ISSN	0217-9849
卷号	34 期号:11 页码:12
摘要	Spectral clustering is one of the most important data processing methods which has been wildly applied to machine learning, computer vision, pattern recognition and image processing. However, one of the main drawbacks of spectral clustering is the fact that the clustering model is defined only for primal data without clear extension to out-of-sample data. To improve its efficiency, in this paper, we proposed a new modularity-based method for spectral clustering with out-of-sample extension. First, kernel independent component analysis is used to solve the demixing matrix on Stiefel manifold in order to extract high-order irrelevant data feature. Then, a new modularity similarity measure-based spectral mapping algorithm is proposed, which allows the clustering model to be directly extended to out-of-sample data. Based on above analysis, we present a spectral clustering algorithm with out-of-sample extension. Experimental results show our method has better performance compared with other related algorithms in different datasets.
关键词	Spectral clustering out-of-sample modularity similarity measure
DOI	10.1142/S0217984920501055
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Project[2017YFD0401001] ; National Key Research and Development Project[2017YFD0700501] ; National Key Research and Development Project[2018YFD0401404] ; National Natural Science Foundation of China[71871233] ; Beijing Natural Science Foundation[9182015]
WOS研究方向	Physics
WOS类目	Physics, Applied ; Physics, Condensed Matter ; Physics, Mathematical
WOS记录号	WOS:000528496600006
出版者	WORLD SCIENTIFIC PUBL CO PTE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/51349
专题	中国科学院数学与系统科学研究院
通讯作者	Yan, Guan
作者单位	1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China 2.Nanjing Univ Finance & Econ, Sch Informat Engn, Nanjing 210003, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Shen, Dongqin,Li, Xiuyi,Yan, Guan. Improve the spectral clustering by integrating a new modularity similarity index and out-of-sample extension[J]. MODERN PHYSICS LETTERS B,2020,34(11):12.
APA	Shen, Dongqin,Li, Xiuyi,&Yan, Guan.(2020).Improve the spectral clustering by integrating a new modularity similarity index and out-of-sample extension.MODERN PHYSICS LETTERS B,34(11),12.
MLA	Shen, Dongqin,et al."Improve the spectral clustering by integrating a new modularity similarity index and out-of-sample extension".MODERN PHYSICS LETTERS B 34.11(2020):12.