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

08/19/2016
by   Yang Wang, et al.
University of Technology Sydney
The University of Adelaide
UNSW
The University of Melbourne
0

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.

READ FULL TEXT
09/05/2017

Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization

Multi-view data clustering attracts more attention than their single vie...
01/30/2019

Feature Concatenation Multi-view Subspace Clustering

Many multi-view clustering methods have been proposed with the popularit...
08/31/2020

Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix

Multi-view spectral clustering can effectively reveal the intrinsic clus...
08/23/2015

MultiView Diffusion Maps

In this study we consider learning a reduced dimensionality representati...
04/30/2020

Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation

This paper explores the problem of multi-view spectral clustering (MVSC)...
05/12/2020

Agglomerative Neural Networks for Multi-view Clustering

Conventional multi-view clustering methods seek for a view consensus thr...

Please sign up or login with your details

Forgot password? Click here to reset