论文标题
双图正规化多视图子空间群集
Double Graphs Regularized Multi-view Subspace Clustering
论文作者
论文摘要
近年来，人们对多视图子空间聚类的学术兴趣越来越大。在本文中，我们提出了一个新颖的双图，正规化多视图子空间聚类（DGRMSC）方法，该方法旨在在统一的框架中利用多视图数据的全局和局部结构信息。具体而言，DGRMSC首先学习一个潜在表示，以利用多种视图的全局互补信息。基于博学的潜在表示，我们学习了一种自我代理来探索其全球聚类结构。此外，双图正则化（DGR）在潜在表示和自我代理上同时同时利用其局部歧管结构。然后，我们设计了一种迭代算法来有效地解决优化问题。对现实世界数据集的广泛实验结果证明了该方法的有效性。
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.
