电子学报 ›› 2013, Vol. 41 ›› Issue (6): 1219-1224.DOI: 10.3969/j.issn.0372-2112.2013.06.028

• 科研通信 • 上一篇    下一篇

一种基于矩阵低秩近似的聚类集成算法

徐森1, 周天2, 于化龙3, 李先锋1   

  1. 1. 盐城工学院信息工程学院, 江苏 盐城 224051;
    2. 哈尔滨工程大学水声技术重点实验室, 黑龙江 哈尔滨 150001;
    3. 江苏科技大学计算机科学与工程学院, 江苏 镇江 212003
  • 收稿日期:2012-05-03 修回日期:2012-10-21 出版日期:2013-06-25 发布日期:2013-06-25
  • 通讯作者: 徐 森 男,1983年1月出生于江苏省滨海县.现为盐城工学院副教授,从事模式识别与智能信息处理方面的研究工作. E-mail:xusen@ycit.cn
  • 作者简介:周 天 男,1979年7月出生于江苏省响水县.现为哈尔滨工程大学副教授,硕士生导师,从事智能信息处理和目标探测与识别方面的研究工作. E-mail:zhoutian@hrbeu.edu.cn
  • 基金资助:

    国家自然科学基金(No.60970542,No.41006057,No.6110507);国家863重点项目(No.2008A09701);国际科技合作聘专重点项目;江苏省高校"青蓝工程"资助项目;盐城工学院人才引进专项基金(No.XKR2011019)

Matrix Low Rank Approximation-Based Cluster Ensemble Algorithm

XU Sen1, ZHOU Tian2, YU Hua-long3, LI Xian-feng1   

  1. 1. School of Information Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu 224051, China;
    2. Science and technology on Underwater Acoustic Laboratory, Harbin Engineering University, Harbin, Heilongjiang 150001, China;
    3. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China
  • Received:2012-05-03 Revised:2012-10-21 Online:2013-06-25 Published:2013-06-25

摘要: 首先将聚类集成问题归结为直观的最佳子空间的求解问题;随后根据线性代数理论将该问题描述为带约束条件的优化问题,通过放松离散约束条件进一步约简为矩阵低秩近似问题;最后通过求解超图的加权邻接矩阵的奇异值分解问题获得最佳子空间的一组标准正交基.据此,设计了一个基于矩阵低秩近似的算法,该算法根据每个对象在低维空间下的坐标使用K均值算法进行聚类,从而得到最终的结果.在多组基准数据集上的实验结果表明:较之于传统的聚类集成算法,本文的算法获得了更好的聚类结果,且效率较高.

关键词: 无监督学习, 聚类分析, 聚类集成, 矩阵低秩近似

Abstract: As an important extension to conventional clustering algorithms,cluster ensemble techniques became a hotspot in machine learning area.In this paper,cluster ensemble problem was first viewed as a direct problem of seeking the best subspace.And then,we formally described the problem as an optimization problem with constraint according to linear algebra,and further transformed into a matrix low rank approximation problem by relaxing the discrete constraint.Lastly,a set of orthonormal basis of the best subspace was attained by solving the singular value decomposition problem of the hypergraph's weighted adjacent matrix.Hereby,a matrix low rank approximation-based algorithm was proposed,which called K-means algorithm to cluster objects according to their coordinates in the low dimensional space and obtained the final clustering result.Experiments on baseline datasets demonstrate the effectiveness of the proposed algorithm,and it outperforms other baseline algorithms.

Key words: unsupervised learning, clustering analysis, cluster ensemble, matrix low rank approximation

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