电子学报2016，Vol.44Issue(3)：535-540,6.DOI:10.3969/j.issn.0372-2112.2016.03.006
基于特征加权和非负矩阵分解的多视角聚类算法
A Multiview Clustering Algorithm Based on Feature Weighting and Non-negative Matrix Factorization
摘要
Abstract
In order to simultaneously solve the two problems of feature weighting and high dimension in the process of multiview clustering,a multiview clustering algorithm based on feature weighting and non-negative matrix factorization ( FWNMF-MC) is proposed.According to the importance of each feature, FWNMF-MC automatically assigns different weights to different features.Multiview data is mapped from high dimensional space to low dimensional space by factorizing feature matrices into basis matrices and coefficient matrices.In order to take advantage of multiview information to mine cluster structure,the consensus of each view is maximized in low dimensional space.The experiment results show the perfor-mence of FWNMF-MC algorithm is superior to four existing classical multiview clustering algorithms.关键词
多视角数据/聚类/非负矩阵分解/特征权重Key words
multiview data/clustering/non-negative matrix factorization/feature weighting分类
信息技术与安全科学引用本文复制引用
刘正,张国印,陈志远..基于特征加权和非负矩阵分解的多视角聚类算法[J].电子学报,2016,44(3):535-540,6.基金项目
国家自然科学基金（No．60873038；No．71272216）；中央高校基本科研业务费专项资金资助 （）
