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Adaptive group sparse multi-view classification method based on mutual information

delete2025-12-20
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PRE
AI
Y
Yadi Wang
T
Tengfei Zhou
X
Xiaoding Guo
江兵兵 (Bingbing Jiang)
J
Jing–Yu Ji
张军 (Jun Zhang)
DOI:10.1016/j.patcog.2025.112931delete
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Abstract

Abstract

En 中文
• Multi-view feature grouping via class-label specific mutual information. • Adaptive MI-based weights impose sparsity to select key features effectively. • AGSMC fuses grouping and weighting to enhance classification performance. • Fast iterative optimization shows AGSMC’s stability and superior accuracy.

Journal

Pattern Recognition cover
Pattern Recognition
IF:
7.6
Papers:
1.3W
Citations:
4.5W

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C
computer and information engineering
Scholars:
174
Papers: 69
Citations: 0
C
College of Artificial Intelligence
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340
Papers: 140
Citations: 1
S
School of Information Science and Engineering
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443
Papers: 174
Citations: 3
S
School of Data Science
Scholars:
88
Papers: 58
Citations: 0
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