基于视图互信息加权的多视图集成聚类算法  

Multi-view ensemble clustering algorithm based on view-wise mutual information weighting

作　　者：劳景欢 黄栋[1] 王昌栋[2] 赖剑煌[2] LAO Jinghuan;HUANG Dong;WANG Changdong;LAI Jianhuang(College of Mathematics and Informatics,South China Agricultural University,Guangzhou Guangdong 510642,China;School of Computer Science and Engineering,Sun Yat-sen University,Guangzhou Guangdong 510006,China)

机构地区：[1]华南农业大学数学与信息学院,广州510642 [2]中山大学计算机学院,广州510006

出　　处：《计算机应用》2023年第6期1713-1718,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61976097,61876193);广东省自然科学基金资助项目(2021A1515012203)。

摘　　要：现有的多视图聚类算法往往缺乏对各视图可靠度的评估和对视图进行加权的能力,而一些具备视图加权的多视图聚类算法则通常依赖于特定目标函数的迭代优化,其目标函数的适用性及部分敏感超参数调优的合理性均对实际应用有显著影响。针对这些问题,提出一种基于视图互信息加权的多视图集成聚类(MEC-VMIW)算法,主要过程可分为两个阶段,即视图互加权阶段与多视图集成聚类阶段。在视图互信息加权阶段,对数据集进行多次随机降采样,以降低评估加权过程的问题规模,进而构建多视图降采样聚类集合,根据不同视图的聚类结果之间的多轮互评得到视图可靠度评估,并据此对视图进行加权;在多视图集成聚类阶段,对各个视图数据构建基聚类集合,并将多个基聚类集合加权建模至二部图结构,利用高效二部图分割算法得到最终多视图聚类结果。在若干个多视图数据集上的实验结果验证了所提出的多视图集成聚类算法的鲁棒聚类性能。Many of the existing multi-view clustering algorithms lack the ability to estimate the reliability of different views and thus weight the views accordingly,and some multi-view clustering algorithms with view-weighting ability generally rely on the iterative optimization of specific objective function,whose real-world applications may be significantly influenced by the practicality of the objective function and the rationality of tuning some sensitive hyperparameters.To address these problems,a Multi-view Ensemble Clustering algorithm based on View-wise Mutual Information Weighting(MEC-VMIW)was proposed,whose overall process consists of two phases:the view-wise mutual weighting phase and the multi-view ensemble clustering phase.In the view-wise mutual weighting phase,multiple random down-samplings were performed to the dataset,so as to reduce the problem size in the evaluating and weighting process.After that,a set of down-sampled clusterings of multiple views was constructed.And,based on multiple runs of mutual evaluation among the clustering results of different views,the view-wise reliability was estimated and used for view weighting.In the multi-view ensemble clustering phase,the ensemble of base clusterings was constructed for each view,and multiple base clustering sets were weighted to model a bipartite graph structure.By performing efficient bipartite graph partitioning,the final multi-view clustering results were obtained.Experiments on several multi-view datasets confirm the robust clustering performance of the proposed multi-view ensemble clustering algorithm.

关 键 词：数据聚类 多视图聚类 互信息 集成聚类 视图加权 二部图 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]