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Group sparse regularization for adaptive multi-view feature selection
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DOI：10.1016/j.asoc.2026.115846.png)
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Abstract
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• Features are grouped via density-based clustering with automatic group estimation. • A novel group-based regularization term is proposed to highlight important feature groups and weaken unimportant ones. • An adaptive multi-view feature selection model is proposed in a unified framework. • An efficient iterative algorithm is developed to solve the proposed optimization model.
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6.6
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