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.