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An Interpretable Deep Unfolding Framework for Multi-view Representation Learning
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AI
DOI：10.1016/j.inffus.2026.104242.png)
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Abstract
En 中文
• Propose an interpretable deep unfolding framework for multi-view representation learning • Ensure four key properties: consistency, topology, diversity, and complementarity • Derive interpretable networks by unfolding property-driven iterative solutions
Keywords:
Interpretable deep unfolding
Multi-view representation learning
Iterative solutions
Consistency
Complementarity
Journal
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