Algorithms for nonnegative matrix factorization with the Kullback-Leibler divergence
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Publication:2031873
DOI10.1007/s10915-021-01504-0zbMath1472.65054arXiv2010.01935OpenAlexW3160314930MaRDI QIDQ2031873
Le Thi Khanh Hien, Nicolas Gillis
Publication date: 15 June 2021
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2010.01935
Related Items (3)
Block Bregman Majorization Minimization with Extrapolation ⋮ Non-negative low-rank approximations for multi-dimensional arrays on statistical manifold ⋮ Unnamed Item
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Cites Work
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- Proximal alternating linearized minimization for nonconvex and nonsmooth problems
- Global convergence of modified multiplicative updates for nonnegative matrix factorization
- Lectures on convex optimization
- Non-negative matrix factorization with fixed row and column sums
- On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing
- A first-order primal-dual algorithm for convex problems with applications to imaging
- Nonnegative matrix factorization and I-divergence alternating minimization
- Document clustering using nonnegative matrix factorization
- A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization
- A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion
- Algorithms for Nonnegative Matrix Factorization with the β-Divergence
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- Accelerating Nonnegative Matrix Factorization Algorithms Using Extrapolation
- Nonnegative Matrix Factorization with the Itakura-Saito Divergence: With Application to Music Analysis
- Relatively Smooth Convex Optimization by First-Order Methods, and Applications
- A Flexible and Efficient Algorithmic Framework for Constrained Matrix and Tensor Factorization
- On Tensors, Sparsity, and Nonnegative Factorizations
- Novel Proximal Gradient Methods for Nonnegative Matrix Factorization with Sparsity Constraints
- Learning the parts of objects by non-negative matrix factorization
- Composite Self-Concordant Minimization
- A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications
- Benchmarking optimization software with performance profiles.
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