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Adaptive group sparse multi-view classification method based on mutual information
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DOI：10.1016/j.patcog.2025.112931.png)
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Abstract
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• 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.
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