  Multi-view spectral clustering, which aims at yielding an agreement or
consensus data objects grouping across multi-views with their graph laplacian
matrices, is a fundamental clustering problem. Among the existing methods,
Low-Rank Representation (LRR) based method is quite superior in terms of its
effectiveness, intuitiveness and robustness to noise corruptions. However, it
aggressively tries to learn a common low-dimensional subspace for multi-view
data, while inattentively ignoring the local manifold structure in each view,
which is critically important to the spectral clustering; worse still, the
low-rank minimization is enforced to achieve the data correlation consensus
among all views, failing to flexibly preserve the local manifold structure for
each view. In this paper, 1) we propose a multi-graph laplacian regularized LRR
with each graph laplacian corresponding to one view to characterize its local
manifold structure. 2) Instead of directly enforcing the low-rank minimization
among all views for correlation consensus, we separately impose low-rank
constraint on each view, coupled with a mutual structural consensus constraint,
where it is able to not only well preserve the local manifold structure but
also serve as a constraint for that from other views, which iteratively makes
the views more agreeable. Extensive experiments on real-world multi-view data
sets demonstrate its superiority.
