  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 don't 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.
