Local and global structure double fusion embedding deep non-negative matrix factorization for multi-view clustering

Songtao Li

doi:10.1016/j.eswa.2025.130208

Local and global structure double fusion embedding deep non-negative matrix factorization for multi-view clustering

2025-11-06

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DOI：10.1016/j.eswa.2025.130208

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• Proposes LFGNMF framework integrating local and global graph structures for multi-view clustering. • Introduces dual adaptive graph regularization to optimize subspace learning and fusion jointly. • Develops efficient optimization algorithm with proven convergence and superior clustering accuracy.

Local and global structure double fusion embedding deep non-negative matrix factorization for multi-view clustering

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