arXiv Machine Learning
Separable Nonnegative Matrix Factorization Using Powered Ratio-of-Norms Regularization
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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.
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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.
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arXiv Machine Learning
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Non--negative matrix factorization using the \textit{R} package \textsf{nnmf}
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