Paper Info
Title
Robust Subspace Clustering Network With Dual-Domain Regularization
Abstract
The field of deep subspace clustering has advanced rapidly in recent years. Ideas such as self-expression and self-supervision have led to innovative network design and improved clustering performance. However, it is observed that the nonlinear low-dimensional manifold constraint is valid in not only the ambient but also the latent feature spaces. Meanwhile, the issue of robustness has been largely overlooked in the literature of deep subspace clustering despite previous studies on robust model-based clustering. Based on these two observations, we present a robust subspace clustering network (RSCN) based on a novel hybrid loss function with dual-domain regularization. On the one hand, we propose to replace the existing L2 loss by a robust hybrid function inspired by half-quadratic minimization; on the other hand, we come up with a novel strategy of sparsity regularization in the dual domain (both ambient and feature space). To the best of our knowledge, this is the first attempt to incorporate dual manifold constraints into deep subspace clustering. Experimental results show that our new network outperforms the existing state-of-the-art on several widely-studied datasets such as Extended Yale B, COIL20, and COIL100. The performance gain of our RSCN over several other competing approaches improves dramatically in the presence of noise contamination. (C) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.patrec.2021.06.009
PATTERN RECOGNITION LETTERS
Keywords
DocType
Volume
Deep subspace clustering, Self-expressive clustering, Self-supervised clustering, Robust clustering, Dual domain regularization
Journal
149
ISSN
Citations 
PageRank 
0167-8655
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Fangfang Wu100.34
Yuan Peng2169.97
Guangming Shi32663184.81
Xin Li45110.28
weisheng dong a5170666.10
Jinjian Wu653342.70