计算机科学与探索2016，Vol.10Issue(4)：554-564,11.DOI:10.3778/j.issn.1673-9418.1505041
面向多视角数据的极大熵聚类算法
Maximum Entropy Clustering Algorithm for Multi-View Data
摘要
Abstract
Currently, the maximum entropy clustering (MEC) merges the multi-view samples to process the multi-view clustering task. However, this will damage the independence of each view, and affect the final partition results. Aiming at this problem, this paper proposes a multi-view collaborative partition maximum entropy clustering (CoMEC) algorithm, which joins a constraint to coordinate each perspective space partition, to make each view in a separate clus-tering process consider the influence of other views. Then this paper proposes the enhanced weighted view version called W-CoMEC by identifying the importance of each view. Finally this paper applies the geometric average integra-tion strategy to obtain the global partition results. The experimental results on a synthetic multi-view dataset and several UCI real-world multi-view datasets show that the proposed algorithm outperforms or is at least comparable to the existing clustering technology in dealing with multi-view clustering task.关键词
熵/多视角聚类/划分/权值/集成策略/UCI数据集Key words
entropy/multi-view clustering/partition/weight/integration strategy/UCI dataset分类
计算机与自动化引用本文复制引用
张丹丹,邓赵红,王士同..面向多视角数据的极大熵聚类算法[J].计算机科学与探索,2016,10(4):554-564,11.基金项目
The National Natural Science Foundation of China under Grant No.61170122(国家自然科学基金) （国家自然科学基金）
the New Century Excellent Tal-ent Foundation from MOE of China under Grant No. NCET-12-0882(教育部新世纪优秀人才支持计划) （教育部新世纪优秀人才支持计划）
