  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.
