摘要
非负矩阵分解模型是一种常见的数据降维方法。在现有非负矩阵分解算法用于聚类的研究中,每个类别一般仅由一个或者指定多个中心点表示,然而这种表示方式往往无法准确描述其类别的特征和结构,从而影响聚类效果。为了解决这个问题,本文提出了峰值点非负矩阵分解算法。该算法首先为数据集找到多个密度峰值点,并构建密度峰值点和样本点的二部图,然后利用二部图完成聚类。此外该算法引入流形图正则化项来充分利用数据间的流形结构信息,并给出了算法的迭代更新规则。在大量真实数据集上的实验结果表明,该方法可以更加有效地利用数据本身的结构信息,从而提高聚类效果。
The non-negative matrix factorization model is a common data dimensionality reduction method.In the existing non-negative matrix factorization algorithm for clustering research,each category is generally represented by only one or more designated center points.However,this type of representation often fails to accurately describe the characteristics and structure of its category,which affects the clustering performance.In order to solve this problem,we proposed the peaks non-negative matrix factorization(PNMF)algorithm.The algorithm first finds multiple density peak points for the dataset,constructs a bipartite graph of the density peak points and sample points,then uses the bipartite graph to complete the clustering.In addition,the algorithm introduces a manifold regularization term to make full use of the manifold structure information between the data,and gives the iterative update rules of the algorithm.Sufficient experiments on real-world datasets demonstrate that the proposed method can effectively utilize the structural information of data and improve the clustering performance.
作者
徐晓华
方威
何萍
仁祥
姜玉麟
葛方毅
XU Xiaohua;FANG Wei;HE Ping;REN Xiang;JIANG Yulin;GE Fangyi(College of Information Engineering,Yangzhou University,Yangzhou 225000,China)
出处
《南京航空航天大学学报》
CAS
CSCD
北大核心
2021年第5期772-779,共8页
Journal of Nanjing University of Aeronautics & Astronautics
基金
国家自然科学基金(61402395)资助项目
江苏省自然科学基金(BK20201430,BK20151314,BK20140492)资助项目。
关键词
非负矩阵分解
降维
密度峰值
图正则
聚类分析
non-negative matrix factorization
dimensionality reduction
density peaks
graph regularization
clustering analysis
