Introduces a topological regularization framework for NMF that uses persistent homology to enforce desired structures in basis functions within a unified optimization objective.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Two heuristic algorithms (fixed-point from penalized KKT and staged ADAM) are proposed for symmetric multi-type orthogonal NMF tri-factorization and evaluated on synthetic noisy data and citation networks for recovery and downstream tasks.
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Non-negative Matrix Factorisation with Topological Regularisation
Introduces a topological regularization framework for NMF that uses persistent homology to enforce desired structures in basis functions within a unified optimization objective.
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On solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem
Two heuristic algorithms (fixed-point from penalized KKT and staged ADAM) are proposed for symmetric multi-type orthogonal NMF tri-factorization and evaluated on synthetic noisy data and citation networks for recovery and downstream tasks.