Multiview clustering is an important topic in multiview learning, where the cooperation of different views is used to improve clustering performance. Although multiview clustering has made considerable progress, most existing methods only utilize the information of the original visible views, or only consider some hidden space information shared by different views. Two of the challenges are: 1) insufficient exploitation of cooperative learning between visible and hidden information despite some preliminary attempts, and 2) inadequate consideration of topological information for improving multiview clustering. To meet the challenges, we propose the cooperation enhanced multiview fuzzy clustering method (CE-MVFC) in this article. First, we characterize multiview data with two hidden views, which are obtained by adaptive multiview non-negative matrix factorization (NMF) and fuzzy partition information of each sample in different clusters. Then, we integrated the hidden views and the original visible views to realize visible-hidden cooperation learning. Furthermore, we establish a similarity matrix for each visible view and the hidden view obtained through NMF to describe the data topology in these views. Based on the spatial topological relationship of the samples and the representation of hidden view obtained by fuzzy partition, the network least absolute shrinkage and selection operator is constructed to constrain multiview learning. Finally, we develop the multiview clustering method by exploiting the visible-hidden information cooperation and the spatial topological information constraints. Experiments on benchmark multiview datasets are conducted to demonstrate the highly competitive performance of the proposed CE-MVFC against the state-of-the-art methods.

中文翻译：

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使用双可见-隐藏视图协作和网络LASSO约束的增强多视图模糊聚类


多视图聚类是多视图学习中的一个重要主题，其中不同视图的协作用于提高聚类性能。尽管多视图聚类已经取得了长足的进步，但大多数现有方法仅利用原始可见视图的信息，或者仅考虑不同视图共享的一些隐藏空间信息。其中两个挑战是：1）尽管进行了一些初步尝试，但对可见信息和隐藏信息之间的合作学习的利用不足；2）对改进多视图聚类的拓扑信息考虑不充分。为了应对这些挑战，我们在本文中提出了协作增强多视图模糊聚类方法（CE-MVFC）。首先，我们用两个隐藏视图来表征多视图数据，这两个隐藏视图是通过自适应多视图非负矩阵分解（NMF）和不同簇中每个样本的模糊划分信息获得的。然后，我们将隐藏视图和原始可见视图相结合，实现可见-隐藏协作学习。此外，我们为每个可见视图和通过NMF获得的隐藏视图建立相似度矩阵来描述这些视图中的数据拓扑。基于样本的空间拓扑关系和模糊划分得到的隐藏视图表示，构造网络最小绝对收缩和选择算子来约束多视图学习。最后，我们通过利用可见-隐藏信息协作和空间拓扑信息约束来开发多视图聚类方法。在基准多视图数据集上进行实验，以证明所提出的 CE-MVFC 相对于最先进的方法具有高度竞争性的性能。