链接文档中基于子空间分解的高效谱聚类算法  

Efficient spectral clustering algorithm based on subspace decomposition in linked documents

作　　者：原虹[1] 赵丽[1] 王溢琴[1] YUAN Hong;ZHAO Li;WANG Yiqin(School of Information Technology and Engineering,Jinzhong University,Jinzhong Shanxi 030619,China)

机构地区：[1]晋中学院信息技术与工程系,山西晋中030619

出　　处：《太赫兹科学与电子信息学报》2022年第9期965-972,共8页Journal of Terahertz Science and Electronic Information Technology

基　　金：新工科背景下“双师”型师资队伍建设2019年第一批产学合作协同育人资助项目(201901040022);山西省自然科学基金资助项目(201901D22111);山西省教育科学“十三五”规划2020年度‘互联网+教育’专项课题(HLW-20111)。

摘　　要：提出了一种基于子空间分解的高效谱聚类算法。首先,基于共识信息和特定域信息的矩阵分解将链接文档划分为3个子空间,然后对子空间添加正则化项建模共识信息和特定域信息对聚类的不同影响,并采用交替优化方法实现谱聚类。考虑到谱聚类的复杂性,提出了一种带曲线搜索的梯度下降法加速求解过程。3个真实数据集上的实验结果表明,所提算法在聚类质量和效率方面始终明显优于目前典型的基线算法,且对输入参数不敏感。An efficient spectral clustering algorithm based on subspace decomposition is proposed.Firstly,based on the matrix decomposition of consensus information and domain specific information,the linked documents are divided into three subspaces,the different effects of consensus information and domain specific information on clustering are modeled by adding regularization items to the subspaces,and the alternative optimization method is utilized to achieve spectral clustering.In addition,considering the complexity of spectral clustering,a gradient descent method with curvilinear search is proposed to accelerate the solution process.Experimental results on three real datasets show that the proposed algorithm is superior to the current typical baseline algorithm in terms of clustering quality and efficiency,and is insensitive to input parameters.

关 键 词：链接文档 子空间分解 谱聚类 梯度下降法 基线算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]