Iterative Views Agreement: An Iterative Low Rank Based Structured Optimization Method to Multi View Spectral Clustering
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.
IJCAI