Leveraging the consensus and complementary principle to find a common representation for different views is an essential problem of multi-view clustering. To address the problem, many Low-Rank Representation (LRR) based methods have been proposed. However, existing LRR based methods have two common limitations: 1) they adopt graph regularization that only considers simple pairwise similarities among data points, and 2) they do not generally characterize the importance of each view. In this letter, we correspondingly utilize hypergraph regularization and a self-weighting strategy to handle the limitations with an LRR based model. Specifically, in our model, we construct hypergraph Laplacian matrices of each view that explicitly contain high order relations among data points, to improve the usage of complementary information. Meanwhile, the self-weighting strategy that preserves view specific information and assigns adaptive weights to each view is leveraged to take full advantage of multi-view consensus information. Based on the Augmented Lagrangian Multiplier (ALM) scheme, we design an effective alternating iterative strategy to optimize the model. Extensive experiments conducted on four benchmark datasets validate the superiority of our method.

中文翻译：

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多视图谱聚类的自加权和超图正则化

利用共识和互补原则为不同的视图找到一个共同的表示是多视图聚类的一个基本问题。为了解决这个问题，已经提出了许多基于低秩表示（LRR）的方法。然而，现有的基于 LRR 的方法有两个共同的局限性：1）它们采用图正则化，只考虑数据点之间简单的成对相似性，2）它们通常不表征每个视图的重要性。在这封信中，我们相应地利用超图正则化和自加权策略来处理基于 LRR 的模型的局限性。具体来说，在我们的模型中，我们构建了每个视图的超图拉普拉斯矩阵，这些矩阵明确包含数据点之间的高阶关系，以改善补充信息的使用。同时，保留视图特定信息并为每个视图分配自适应权重的自加权策略被用来充分利用多视图共识信息。基于增广拉格朗日乘子（ALM）方案，我们设计了一种有效的交替迭代策略来优化模型。在四个基准数据集上进行的大量实验验证了我们方法的优越性。