With the advance of technology, entities can be observed in multiple views. Multiple views containing different types of features can be used for clustering. Although multi-view clustering has been successfully applied in many applications, the previous methods usually assume the complete instance mapping between different views. In many real-world applications, information can be gathered from multiple sources, while each source can contain multiple views, which are more cohesive for learning. The views under the same source are usually fully mapped, but they can be very heterogeneous. Moreover, the mappings between different sources are usually incomplete and partially observed, which makes it more difficult to integrate all the views across different sources. In this paper, we propose MMC (Multi-source Multi-view Clustering), which is a framework based on collective spectral clustering with a discrepancy penalty across sources, to tackle these challenges. MMC has several advantages compared with other existing methods. First, MMC can deal with incomplete mapping between sources. Second, it considers the disagreements between sources while treating views in the same source as a cohesive set. Third, MMC also tries to infer the instance similarities across sources to enhance the clustering performance. Extensive experiments conducted on real-world data demonstrate the effectiveness of the proposed approach.

本文提出了一种基于跨源差异惩罚的集体谱聚类框架—— MMC (多源多视角聚类) 来解决多源数据的多视角聚类问题，该方法考虑了视角间不完全的映射、相容性以及跨源实例相似性等方面，实验表明相比其他方法，MMC的聚类性能更为有效。

多源多视角聚类中的差异惩罚方法