The graph-based incomplete multi-view clustering aims at integrating information from multiple views and utilizes graph models to capture the global and local structure of the data for reconstructing missing data, which is suitable for processing complex data. However, ordinary graph learning methods usually only consider pairwise relationships between data points and cannot unearth higher-order relationships latent in the data. And existing graph clustering methods often divide the process of learning the representations and the clustering process into two separate steps, which may lead to unsatisfactory clustering results. Besides, they also tend to consider only intra-view similarity structures and overlook inter-view ones. To this end, this paper introduces an innovative one-step incomplete multi-view clustering based on hypergraph (IMVC_HG). Specifically, we use a hypergraph to reconstruct missing views, which can better explore the local structure and higher-order information between sample points. Moreover, we use non-negative matrix factorization with orthogonality constraints to equate K-means, which eliminates post-processing operations and avoids the problem of suboptimal results caused by the two-step approach. In addition, the tensor Schatten p-norm is used to better capture the complementary information and low-rank structure between the cluster label matrices of multiple views. Numerous experiments verify the superiority of IMVC_HG.

中文翻译：

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基于 Hypergraph 的不完整多视图聚类


基于图的不完全多视图聚类旨在整合来自多个视图的信息，并利用图模型来捕获数据的全局和局部结构，用于重建缺失数据，适用于处理复杂数据。然而，普通的图学习方法通常只考虑数据点之间的成对关系，无法挖掘数据中隐藏的高阶关系。而现有的图聚类方法往往将学习表示的过程和聚类过程分为两个独立的步骤，这可能会导致聚类结果不尽如人意。此外，他们也倾向于只考虑视图内的相似性结构，而忽略视图间的相似性结构。为此，本文介绍了一种基于 Hypergraph （IMVC_HG） 的创新一步不完整多视图聚类。具体来说，我们使用超图来重建缺失的视图，这可以更好地探索采样点之间的局部结构和高阶信息。此外，我们使用具有正交性约束的非负矩阵分解来等同于 K-means，这消除了后处理操作并避免了由两步方法引起的次优结果问题。此外，张量 Schatten p-norm 用于更好地捕获多个视图的聚类标签矩阵之间的互补信息和低秩结构。大量实验验证了 IMVC_HG 的优越性。