Paper Info
Title
On Reliable Multi-View Affinity Learning for Subspace Clustering
Abstract
In multi-view subspace clustering, the low-rankness of the stacked self-representation tensor is widely accepted to capture the high-order cross-view correlation. However, using the nuclear norm as a convex surrogate of the rank function, the self-representation tensor exhibits strong connectivity with dense coefficients. When noise exists in the data, the generated affinity matrix may be unreliable for subspace clustering as it retains the connections across inter-cluster samples due to the lack of sparsity. Since both the connectivity and sparsity of the self-representation coefficients are curial for subspace clustering, we propose a Reliable Multi-View Affinity Learning (RMVAL) method so as to optimize both properties in a single model. Specifically, RMVAL employs the low-rank tensor constraint to yield a well-connected yet dense solution, and purifies the densely connected self-representation tensor by preserving only the connections in local neighborhoods using the l(1)-norm regularization. This way, the strong connections on the self-representation tensor are retained and the trivial coefficients corresponding to the inter-cluster connections are suppressed, leading to a "clean" self-representation tensor and also a reliable affinity matrix. We propose an efficient algorithm to solve RMVAL using the alternating direction method of multipliers. Extensive experiments on benchmark databases have demonstrated the superiority of RMVAL.
Year
DOI
Venue
2021
10.1109/TMM.2020.3045259
IEEE TRANSACTIONS ON MULTIMEDIA
Keywords
DocType
Volume
Affinity learning, connectivity and sparsity, low-rank tensor, multi-view subspace clustering, self-representation
Journal
23
Issue
ISSN
Citations 
1
1520-9210
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xiaolin Xiao1366.57
Yue-jiao Gong269141.19
Zhongyun Hua335520.35
Wei-Neng Chen4110354.10