标题：
Doubly aligned incomplete multi-view clustering
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所属单位：
计算机科学与技术学院/人工智能学院/软件学院
发表刊物：
IJCAI Int. Joint Conf. Artif. Intell.
摘要：
Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clustering methods. In this paper, we propose a Doubly Aligned Incomplete Multi-view Clustering algorithm (DAIMC) based on weighted semi-nonnegative matrix factorization (semi-NMF). Specifically, on the one hand, DAIMC utilizes the given instance alignment information to learn a common latent feature matrix for all the views. On the other hand, DAIMC establishes a consensus basis matrix with the help of L2,1-Norm regularized regression for reducing the influence of missing instances. Consequently, compared with existing methods, besides inheriting the strength of semi-NMF with ability to handle negative entries, DAIMC has two unique advantages: 1) solving the incomplete view problem by introducing a respective weight matrix for each view, making it able to easily adapt to the case with more than two views; 2) reducing the influence of view incompleteness on clustering by enforcing the basis matrices of individual views being aligned with the help of regression. Experiments on four real-world datasets demonstrate its advantages. © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.
ISSN号：
1045-0823
是否译文：
否
发表时间：
2018-01-01
合写作者：
Hu, Menglei
通讯作者：
陈松灿
发表时间：
2018-01-01
学位：工学博士学位
职称：教授
所在单位：计算机科学与技术学院/人工智能学院/软件学院