Consensus Graph Learning for Incomplete Multi-view Clustering

摘要：

　　Multi-view data clustering is a fundamental task in current machine learning,known as multi-view clustering.Existing multi-view clustering methods mostly assume that each data instance is sampled in all views.However,in real-world applications,it is common that certain views miss number of data instances,resulting in incomplete multi-view data.This paper concerns the task of clustering of incomplete multi-view data.We propose a novel Graph-based Incomplete Multi-view Clustering(GIMC)to perform this task.GIMC can effectively construct a complete graph for each view with the help of other view(s),and automatically weight each constructed graph to learn a consensus graph,which gives the final clusters.An alternating iterative optimization algorithm is proposed to optimize the objective function.Experimental results on real-world datasets show that the proposed method outperforms state-of-the-art baseline methods markedly.

关键词： Multi-view clustering Incomplete views Graph-based clustering

作者: Wei Zhou Hao Wang Yan Yang

作者单位: School of Information Science and Technology,Southwest Jiaotong University,Chengdu,China

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 529-540

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）