A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data. Among them, spectral clustering-based methods have drawn much attention and demonstrated promising results recently. Despite progress, there are still two fundamental questions that stay unanswered to date. First, how to fuse different views into one graph. More often than not, the similarities between samples may be manifested differently by different views. Many existing algorithms either simply take the average of multiple views or just learn a common graph. These simple approaches fail to consider the flexible local manifold structures of all views. Hence, the rich heterogeneous information is not fully exploited. Second, how to learn the explicit cluster structure. Most existing methods do not pay attention to the quality of the graphs and perform graph learning and spectral clustering separately. Those unreliable graphs might lead to suboptimal clustering results. To fill these gaps, in this paper, we propose a novel multi-view spectral clustering model which performs graph fusion and spectral clustering simultaneously. The fusion graph approximates the original graph of each individual view but maintains an explicit cluster structure. Experiments on four widely used data sets confirm the superiority of the proposed method.

已经开发出了全视图的多视图聚类算法来处理流行的多视图数据。其中，基于谱聚类的方法最近引起了广泛关注，并显示出可喜的结果。尽管取得了进展，但到目前为止，仍有两个基本问题尚未解答。首先，如何将不同的视图融合到一张图中。通常，样本之间的相似性可能通过不同的观点以不同的方式体现出来。许多现有算法要么简单地取多个视图的平均值，要么仅学习一个公共图。这些简单的方法无法考虑所有视图的灵活局部流形结构。因此，丰富的异构信息未被充分利用。第二，如何学习显式集群结构。现有的大多数方法都不关注图形的质量，而是分别进行图形学习和频谱聚类。这些不可靠的图可能会导致聚类结果欠佳。为了填补这些空白，在本文中，我们提出了一种新颖的多视图光谱聚类模型，该模型同时执行图融合和光谱聚类。融合图近似于每个单独视图的原始图，但保持显式的簇结构。在四个广泛使用的数据集上进行的实验证实了该方法的优越性。融合图近似于每个单独视图的原始图，但保持显式的簇结构。在四个广泛使用的数据集上进行的实验证实了该方法的优越性。融合图近似于每个单独视图的原始图，但保持显式的簇结构。在四个广泛使用的数据集上进行的实验证实了该方法的优越性。