Recent years have witnessed a growing academic interest in multi-view subspace clustering. In this paper, we propose a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to harness both global and local structural information of multi-view data in a unified framework. Specifically, DGRMSC firstly learns a latent representation to exploit the global complementary information of multiple views. Based on the learned latent representation, we learn a self-representation to explore its global cluster structure. Further, Double Graphs Regularization (DGR) is performed on both latent representation and self-representation to take advantage of their local manifold structures simultaneously. Then, we design an iterative algorithm to solve the optimization problem effectively. Extensive experimental results on real-world datasets demonstrate the effectiveness of the proposed method.
@article{arxiv.2209.15143,
title = {Double Graphs Regularized Multi-view Subspace Clustering},
author = {Longlong Chen and Yulong Wang and Youheng Liu and Yutao Hu and Libin Wang},
journal= {arXiv preprint arXiv:2209.15143},
year = {2022}
}