基于低秩约束的熵加权多视角模糊聚类算法  

Entropy-weighting Multi-view Fuzzy C-means With Low Rank Constraint

作　　者：张嘉旭 王骏[1,2] 张春香 林得富 周塔 王士同 ZHANG Jia-Xu;WANG Jun;ZHANG Chun-Xiang;LIN De-Fu;ZHOU Ta;WANG Shi-Tong(School of Digital Media,Jiangnan University,Wuxi 214122;School of Communication and Information Engineering,Shanghai University,Shanghai 200444;School of Electronic Information,Jiangsu University of Science and Technology,Zhenjiang 212100)

机构地区：[1]江南大学数字媒体学院,无锡214122 [2]上海大学通信与信息工程学院,上海200444 [3]江苏科技大学电子信息学院,镇江212100

出　　处：《自动化学报》2022年第7期1760-1770,共11页Acta Automatica Sinica

基　　金：国家自然科学基金(61772239);江苏省自然科学基金(BK20181339)资助。

摘　　要：如何有效挖掘多视角数据内部的一致性以及差异性是构建多视角模糊聚类算法的两个重要问题.本文在Co-FKM算法框架上,提出了基于低秩约束的熵加权多视角模糊聚类算法(Entropy-weighting multi-view fuzzy C-means with low rank constraint,LR-MVEWFCM).一方面,从视角之间的一致性出发,引入核范数对多个视角之间的模糊隶属度矩阵进行低秩约束;另一方面,基于香农熵理论引入视角权重自适应调整策略,使算法根据各视角的重要程度来处理视角间的差异性.本文使用交替方向乘子法(Alternating direction method of multipliers,ADMM)进行目标函数的优化.最后,人工模拟数据集和UCI(University of California Irvine)数据集上进行的实验结果验证了该方法的有效性.Effective mining both internal consistency and diversity of multi-view data is important to develop multiview fuzzy clustering algorithms.In this paper,we propose a novel multi-view fuzzy clustering algorithm called entropy-weighting multi-view fuzzy c-means with low-rank constraint(LR-MVEWFCM).On the one hand,we introduce the nuclear norm as the low-rank constraint of the fuzzy membership matrix.On the other hand,the adaptive adjustment strategy of view weight is introduced to control the differences among views according to the importance of each view.The learning criterion can be optimized by the alternating direction method of multipliers(ADMM).Experimental results on both artificial and UCI(University of California Irvine)datasets show the effectiveness of the proposed method.

关 键 词：多视角模糊聚类 香农熵 低秩约束 核范数 交替方向乘子法 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]