New algorithm speeds up matrix factorization for graph clustering

Researchers have developed SNMPBB, a novel nonmonotone projected Barzilai-Borwein algorithm for Symmetric Nonnegative Matrix Factorization (Symmetric NMF). This new method significantly improves convergence speed compared to existing projected gradient approaches for Symmetric NMF, achieving up to a six-fold speedup on synthetic data. The algorithm has been extended for graph clustering (Graph-SNMPBB) and large-scale problems with low-rank approximations (LAI-SNMPBB), demonstrating competitive accuracy and performance on real-world benchmarks and large matrices. AI

IMPACT Introduces a faster algorithm for matrix factorization, potentially improving performance in downstream machine learning and graph analysis tasks.

RANK_REASON The cluster contains a research paper detailing a new algorithm for matrix factorization and graph clustering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Barzilai-Borwein methods
• Graph-SNMPBB
• LAI-SNMPBB
• LAI-SymPGNCG
• SNMPBB
• SymANLS

• paper
• other

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