LABIN: Balanced Min Cut for Large-Scale Data

被引：30

作者：

Chen, Xiaojun ^{[1
]}

Chen, Renjie ^{[2
]}

Wu, Qingyao ^{[2
]}

Fang, Yixiang ^{[3
]}

Nie, Feiping ^{[4
,5
]}

Huang, Joshua Zhexue ^{[1
]}

机构：

[1] Shenzhen Univ, Coll Comp Sci & Software, Shenzhen 518060, Peoples R China

[2] South China Univ Technol, Sch Software Engn, Guangzhou 510630, Peoples R China

[3] Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China

[4] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China

[5] Northwestern Polytech Univ, Ctr OPTIMAL, Xian 710072, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2020年 / 31卷 / 03期

关键词：

Computational modeling; Clustering algorithms; Computational complexity; Data models; Clustering methods; Laplace equations; Learning systems; Clustering; graph cut; large-scale data; spectral clustering;

D O I：

10.1109/TNNLS.2019.2909425

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Although many spectral clustering algorithms have been proposed during the past decades, they are not scalable to large-scale data due to their high computational complexities. In this paper, we propose a novel spectral clustering method for large-scale data, namely, large-scale balanced min cut (LABIN). A new model is proposed to extend the self-balanced min-cut (SBMC) model with the anchor-based strategy and a fast spectral rotation with linear time complexity is proposed to solve the new model. Extensive experimental results show the superior performance of our proposed method in comparison with the state-of-the-art methods including SBMC.

引用

页码：725 / 736

页数：12

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