摘要
图像聚类是当前的研究热点,非负矩阵分解(non-negative matrix factorization,NMF)算法在图像聚类领域得到了广泛应用。但是单一的NMF算法无法应用于所有数据集,并且NMF算法直接在数据的原始空间进行处理,抗噪能力较差。集成聚类可以解决上述问题,集成聚类将若干个基础聚类结果合成一个一致性结果,不仅可以提高聚类的求解质量,还可以增强算法的鲁棒性。因此本文提出一种层次预处理的NMF加权集成聚类算法。该算法将层次划分、集成聚类和二部图的思想引入到NMF算法中。在预处理阶段,利用层次划分得到聚类数目。之后采用局部加权的方法得到协关联矩阵。最后利用基于二部图的一致性函数进行划分得到最终的聚类结果。在5个数据集上进行实验,验证了本文算法相对于传统算法和其他集成算法的有效性。
Image clustering is a hot research topic at present,and nonnegative matrix factorization(NMF)has been widely used in the field of image clustering.However,a single NMF clustering algorithm can't be applied to all datasets,and the NMF algorithm directly processes the original space of the data,which has poor noise resistance.Ensemble clustering can solve the above problems.Ensemble clustering combines several basic clustering results into a consistent result,which not only improves the quality of clustering,but also enhances the robustness of the algorithm.Therefore,a hierarchical preprocessing NMF weighted integrated clustering algorithm is presented.The algorithm introduces the idea of hierarchical division,ensemble clustering and bipartite graph into the NMF algorithm.In the preprocessing stage,the number of clusters is obtained by hierarchical division.The co-association matrix is then obtained by local weighting.Finally,the final clustering result is obtained by partitioning using the consistency function based on the bipartite graph.The algorithm is tested on five datasets to verify the effectiveness of the algorithm over traditional algorithms and other ensemble algorithms.
作者
李向利
毕胜
王佩源
Xiang li;BI sheng;WANG Peiyuan(School of Mathematics&Computing Science,Guilin University of Electronic Technology;Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation;Center for Applied Mathematics of Guangxi(GUET),Guilin Guangxi 541004,China)
出处
《重庆师范大学学报（自然科学版）》
CAS
北大核心
2023年第5期136-144,共9页
Journal of Chongqing Normal University:Natural Science
基金
国家自然科学基金面上项目(No.11961010,No.61967004)。
关键词
图像聚类
聚类集成
非负矩阵分解
image clustering
ensemble clustering
nonnegative matrix factorization
