Multi-Order Neighborhood Fusion Based Multi-View Deep Subspace Clustering  

作　　者：Kai Zhou Yanan Bai Yongli Hu Boyue Wang 

机构地区：[1]Department of Automation,Tsinghua University,Beijing,100084,China [2]National Center of Technology Innovation for Intelligentization of Politics and Law,Beijing,100000,China [3]Beijing Key Lab of Intelligent Telecommunication Software and Multimedia,Beijing University of Technology,Beijing,100124,China

出　　处：《Computers, Materials & Continua》2025年第3期3873-3890,共18页计算机、材料和连续体(英文)

基　　金：supported by the National Key R&D Program of China(2023YFC3304600).

摘　　要：Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data,while the learned representation is difficult to maintain the underlying structure hidden in the origin samples,especially the high-order neighbor relationship between samples.To overcome the above challenges,this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model.We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module.By this design,the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix;then,we can obtain an optimal global affinity matrix where each connected node belongs to one cluster.In addition,the discriminative constraint between views is designed to further improve the clustering performance.A range of experiments on six public datasets demonstrates that the method performs better than other advanced multi-view clustering methods.The code is available at https://github.com/songzuolong/MNF-MDSC(accessed on 25 December 2024).

关 键 词：Multi-view subspace clustering subspace clustering deep clustering multi-order graph structure 

分 类 号：O65[理学—分析化学]