SOURCE-LINKED INTELLIGENCE

Separable Nonnegative Matrix Factorization Using Powered Ratio-of-Norms Regularization

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

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 eff

Read original source ↗ Open in workspace

recordType

paper

region

Global