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Adaptive group sparse multi-view classification method based on mutual information
DOI:10.1016/j.patcog.2025.112931.png)
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
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7.6
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1.3W
Citations:
4.5W

