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

被引:0
|
作者
Ge, Shaodi [1 ]
Li, Hongjun [1 ]
Luo, Liuhong [1 ]
机构
[1] Beijing Forestry Univ, Coll Sci, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
SPARSE; FRAMEWORK; ALGORITHM; OBJECTS; PARTS;
D O I
10.1155/2019/7565640
中图分类号
T [工业技术];
学科分类号
08 ;
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.
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页数:17
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