Separable Nonnegative Matrix Factorization Using Powered Ratio-of-Norms Regularization
Separable nonnegative matrix factorization (SNMF) has been widely used for low-rank representation and clustering of nonnegative data, owing to its ability to produce part-based and interpretable decompositions. In particular, SNMF is closely related to graph clustering and community detection. To enhance sparsity and identifiability of the learned factors, we propose an $\ell_1^p/\ell_2$-regularized SNMF model based on a powered ratio-of-norms regularizer. The resulting formulation is nonconvex and nonsmooth, which poses significant challenges for optimization. To address this, we develop efficient algorithms based on the difference-of-convex function algorithm (DCA) and the alternating ...
| Authors | Matthew McCarver, Jing Qin |
|---|---|
| Primary category | math.NA |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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