PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau.
Nanchang Institute of Technology, Nanchang 330044, China; Sun Yat-sen University, Guangzhou 510000, China.
Neural Netw. 2021 Jan;133:207-219. doi: 10.1016/j.neunet.2020.10.014. Epub 2020 Nov 11.
Incomplete multi-view clustering which aims to solve the difficult clustering challenge on incomplete multi-view data collected from diverse domains with missing views has drawn considerable attention in recent years. In this paper, we propose a novel method, called consensus guided incomplete multi-view spectral clustering (CGIMVSC), to address the incomplete clustering problem. Specifically, CGIMVSC seeks to explore the local information within every single-view and the semantic consistent information shared by all views in a unified framework simultaneously, where the local structure is adaptively obtained from the incomplete data rather than pre-constructed via a k-nearest neighbor approach in the existing methods. Considering the semantic consistency of multiple views, CGIMVSC introduces a co-regularization constraint to minimize the disagreement between the common representation and the individual representations with respect to different views, such that all views will obtain a consensus clustering result. Experimental comparisons with some state-of-the-art methods on seven datasets validate the effectiveness of the proposed method on incomplete multi-view clustering.
近年来，旨在解决从不同领域采集的具有缺失视图的不完备多视图数据上的困难聚类挑战的不完备多视图聚类引起了相当大的关注。在本文中，我们提出了一种新的方法，称为一致性引导不完备多视图谱聚类（CGIMVSC），以解决不完备聚类问题。具体来说，CGIMVSC 试图同时探索每一个单视图中的局部信息和所有视图之间的语义一致信息，其中局部结构是从不完备数据中自适应获取的，而不是在现有方法中通过 k-最近邻方法预先构建的。考虑到多视图的语义一致性，CGIMVSC 引入了一个共同正则化约束，以最小化关于不同视图的公共表示和各个表示之间的差异，从而使所有视图都能获得一致的聚类结果。在七个数据集上与一些最先进的方法进行的实验比较验证了该方法在不完备多视图聚类上的有效性。
