计算机应用研究2017，Vol.34Issue(6)：1621-1625,5.DOI:10.3969/j.issn.1001-3695.2017.06.005
基于超图和样本自表征的谱聚类算法
Hypergraph and self-representation for spectral clustering
摘要
Abstract
To solve the issue that the traditional spectral clustering methods constructed the similarity matrix by only considering the pairwise relationship of the data but ignoring the complicated correlations among samples,this paper put forward a hypergraph and self-representation based spectral clustering method,called hypergraph and self-representation for spectral clustering (HGSR).Firstly,the algorithm constructed a hypergraph which fully considered the relations of samples to output the hypergraph Laplacian matrix.Secondly,it conducted row sparse self-representation for all samples by utilizing an l2,1-norm regulaxizer,and also put hypergraph Laplacian into the regulation to guarantee the local structure of each sample.In this Way,similax samples were clustered into same cluster.At last,it obtained an affinity matrix for conducting spectral clustering.By utilizing the hypergraph based self-representation,it considered the complicate relationships between the samples.The experimental results of Hopkins155 dataset and some image datasets show that the proposed method outperforms the LSR,SSC and LRR,in terms of the subspace clustering error.关键词
谱聚类/超图/超图拉普拉斯/样本自表征Key words
spectral clustering/hypergraph/hypergraph Laplacian matrix/sample self-representation分类
信息技术与安全科学引用本文复制引用
李永钢,苏毅娟,何威,雷聪..基于超图和样本自表征的谱聚类算法[J].计算机应用研究,2017,34(6):1621-1625,5.基金项目
国家自然科学基金资助项目(61450001,61263035,61573270) （61450001,61263035,61573270）
国家“973”计划资助项目(2013CB329404) （2013CB329404）
中国博士后科学基金资助项目(2015M570837) （2015M570837）
广西自然科学基金资助项目(2012GXNSFGA060004,2015GXNSFCB139011,2015GXNSFAA139306) （2012GXNSFGA060004,2015GXNSFCB139011,2015GXNSFAA139306）
广西研究生教育创新计划资助项目(YCSZ2016045,XYCSZ2017064) （YCSZ2016045,XYCSZ2017064）
