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Robust clean hypergraph for incomplete multi-view clustering

delete2025-11-02
delete0
PRE
AI
Y
Yu Chen
H
Hongmei Chen
B
Biao Xiang
Z
Zhong Yuan
C
Chuan Luo
S
Shi‐Jinn Horng
T
Tianrui Li
DOI：10.1016/j.eswa.2025.130203delete
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Abstract

Abstract

En 中文
• Robustness against noise is enhanced by decomposing the self-representation matrix. • An auto-weighted mechanism constructs a consensus graph to fuse multi-view information. • A clean hypergraph captures the higher-order similarity relationships among data points. • The tensor nuclear norm constraint uncovers the common low-rank structure across multiple views.

Journal

Expert Systems with Applications cover
Expert Systems with Applications
IF:
7.5
Papers:
2.9W
Citations:
10.2W

Organization

U
university
Scholars:
1.9W
Papers: 7.8K
Citations: 3
S
Southwest Jiaotong University
Scholars:
2.9W
Papers: 2.1W
Citations: 2.3W
A
asia university
Scholars:
211
Papers: 210
Citations: 0
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