  Despite the impressive clustering performance and efficiency in
characterizing both the relationship between data and cluster structure,
existing graph-based multi-view clustering methods still have the following
drawbacks. They suffer from the expensive time burden due to both the
construction of graphs and eigen-decomposition of Laplacian matrix, and fail to
explore the cluster structure of large-scale data. Moreover, they require a
post-processing to get the final clustering, resulting in suboptimal
performance. Furthermore, rank of the learned view-consensus graph cannot
approximate the target rank. In this paper, drawing the inspiration from the
bipartite graph, we propose an effective and efficient graph learning model for
multi-view clustering. Specifically, our method exploits the view-similar
between graphs of different views by the minimization of tensor Schatten
p-norm, which well characterizes both the spatial structure and complementary
information embedded in graphs of different views. We learn view-consensus
graph with adaptively weighted strategy and connectivity constraint such that
the connected components indicates clusters directly. Our proposed algorithm is
time-economical and obtains the stable results and scales well with the data
size. Extensive experimental results indicate that our method is superior to
state-of-the-art methods.
