arXiv Machine Learning
Separable Nonnegative Matrix Factorization Using Powered Ratio-of-Norms Regularization
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jun 3
A Nonmonotone Gradient-Based Algorithm for Symmetric Nonnegative Matrix Factorization and Graph Clustering
arXiv:2606. 02887v1 Announce Type: new Abstract: Symmetric nonnegative matrix factorization (Symmetric NMF) approximates a matrix as $WW^T$ with nonnegative rectangular factor $W$.
arXiv Machine Learning
Jul 16
An Efficient Newton Algorithm for Nonnegative Matrix Factorization with the Kullback-Leibler Divergence
arXiv:2607. 13919v1 Announce Type: new Abstract: Nonnegative Matrix Factorization (NMF) is a fundamental tool in unsupervised learning, which approximates a nonnegative matrix by the product of two low-rank nonnegative factors.
arXiv Machine Learning
Jun 9
On solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem
arXiv:2606. 08291v1 Announce Type: new Abstract: We study the symmetric multi-type orthogonal non-negative matrix tri-factorization problem, where several symmetric non-negative matrices are simultaneously approximated by factors of the form $GS_{i}G^{\top}$, with a shared non-negative and orthogonal factor $G$.
arXiv Machine Learning
Jul 7
A Provably-Correct and Robust Convex Model for Smooth Separable NMF
arXiv:2511. 07109v2 Announce Type: replace-cross Abstract: Nonnegative matrix factorization (NMF) is a linear dimensionality reduction technique for nonnegative data, with applications such as hyperspectral unmixing and topic modeling.
arXiv Machine Learning
5d ago
Controlled Block Proximal Linearized Method for Nonconvex and Nonsmooth Optimization
The paper introduces the Controlled Block Proximal Linearized (CBPL) method for multiblock nonconvex and nonsmooth optimization, avoiding assumptions of bounded iterates or objective coercivity. CBPL adds a control potential to block updates while preserving the original objective, variables, and constraints, and under explicit control design conditions and the KL property it guarantees boundedness, finite length, whole‑sequence convergence of variables, and convergence of objective values. The method is applied to graph‑regularized nonnegative matrix factorization, CP decomposition, and independent‑core matrix tri‑factorization with β0 sparsity constraints, demonstrating reconstruction‑preserving scale degeneracies, explicit controls yielding compact sublevel sets, and whole‑sequence convergence to full critical points, with experiments confirming convergence and sustained graph action.
arXiv Machine Learning
Jun 19
Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
arXiv:2512. 17473v3 Announce Type: replace-cross Abstract: We present an algorithm based on the alternating direction method of multipliers (ADMM) for solving nonlinear matrix decompositions (NMD).