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Constrained Dual Graph Regularized Orthogonal Nonnegative Matrix Tri-Factorization for Co-Clustering

机译：约束双图正常化正交非环境矩阵三分化用于共聚类

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摘要

Coclustering approaches for grouping data points and features have recently been receiving extensive attention. In this paper, we propose a constrained dual graph regularized orthogonal nonnegative matrix trifactorization (CDONMTF) algorithm to solve the coclustering problems. The new method improves the clustering performance obviously by employing hard constraints to retain the priori label information of samples, establishing two nearest neighbor graphs to encode the geometric structure of data manifold and feature manifold, and combining with biorthogonal constraints as well. In addition, we have also derived the iterative optimization scheme of CDONMTF and proved its convergence. Clustering experiments on 5 UCI machine-learning data sets and 7 image benchmark data sets show that the achievement of the proposed algorithm is superior to that of some existing clustering algorithms.
机译：用于分组数据点和功能的Coclustering方法最近得到了广泛的关注。在本文中，我们提出了一个受约束的双图正则化正交非环境矩阵三术（CDONMTF）算法来解决Coclustering问题。通过采用硬约束来保留样本的先验标签信息来提高聚类性能，建立两个最接近的邻图，以编码数据歧管的几何结构，并与双正交约束组合。此外，我们还衍生了CDONMTF的迭代优化方案，并证明了其收敛性。在5个UCI机器学习数据集和7个图像基准数据集中的聚类实验表明，所提出的算法的实现优于一些现有聚类算法的实现。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第25期|7565640.1-7565640.17|共17页
  • 作者

    Ge Shaodi; Li Hongjun; Luo Liuhong;

  • 作者单位

    Beijing Forestry Univ Coll Sci Beijing 100083 Peoples R China;

    Beijing Forestry Univ Coll Sci Beijing 100083 Peoples R China;

    Beijing Forestry Univ Coll Sci Beijing 100083 Peoples R China;

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