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

Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering