In recent years, a lot of excellent multi-view clustering methods have been proposed. Because most of them need to fuse all views at one time, they are infeasible as the number of views increases over time. If the present multi-view clustering methods are employed directly to re-fuse all views at each time, it is too expensive to store all historical views. In this paper, we proposed an efficient incremental multi-view spectral clustering method with sparse and connected graph learning (SCGL). In our method, only one consensus similarity matrix is stored to represent the structural information of all historical views. Once the newly collected view is available, the consensus similarity matrix is reconstructed by learning from its previous version and the current new view. To further improve the incremental multi-view clustering performance, the sparse graph learning and the connected graph learning are integrated into our model, which can not only reduce the noises, but also preserve the correct connections within clusters. Experiments on several multi-view datasets demonstrate that our method is superior to traditional methods in clustering accuracy, and is more suitable to deal with the multi-view clustering with the number of views increasing over time.

近年来，提出了很多优秀的多视图聚类方法。因为它们中的大多数需要一次融合所有视图，随着视图数量随着时间的推移而增加，它们是不可行的。如果直接采用目前的多视图聚类方法每次重新融合所有视图，存储所有历史视图的成本太高。在本文中，我们提出了一种具有稀疏和连通图学习（SCGL）的高效增量多视图谱聚类方法。在我们的方法中，只存储一个共识相似度矩阵来表示所有历史视图的结构信息。一旦新收集的视图可用，就会通过从其先前版本和当前新视图中学习来重建共识相似度矩阵。为了进一步提高增量多视图聚类性能，稀疏图学习和连接图学习被集成到我们的模型中，这不仅可以降低噪声，还可以保留簇内的正确连接。在多个多视图数据集上的实验表明，我们的方法在聚类精度上优于传统方法，更适合处理视图数量随时间增加的多视图聚类。