Fast optimization of non-negative matrix tri-factorization
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DOI: 10.1371/journal.pone.0217994
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References listed on IDEAS
- Birgin, Ernesto G. & Martínez, Jose Mario & Raydan, Marcos, 2014. "Spectral Projected Gradient Methods: Review and Perspectives," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i03).
- GILLIS, Nicolas & GLINEUR, François, 2011.
"Accelerated multiplicative updates and hierarchical als algorithms for nonnegative matrix factorization,"
LIDAM Discussion Papers CORE
2011030, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- GILLIS, Nicolas & GLINEUR, François, 2012. "Accelerated multiplicative updates and hierarchical ALS algorithms for nonnegative matrix factorization," LIDAM Reprints CORE 2389, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Berry, Michael W. & Browne, Murray & Langville, Amy N. & Pauca, V. Paul & Plemmons, Robert J., 2007. "Algorithms and applications for approximate nonnegative matrix factorization," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 155-173, September.
- Jingu Kim & Yunlong He & Haesun Park, 2014. "Algorithms for nonnegative matrix and tensor factorizations: a unified view based on block coordinate descent framework," Journal of Global Optimization, Springer, vol. 58(2), pages 285-319, February.
- Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
- NESTEROV, Yurii, 2012. "Efficiency of coordinate descent methods on huge-scale optimization problems," LIDAM Reprints CORE 2511, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- M. Merritt & Y. Zhang, 2005. "Interior-Point Gradient Method for Large-Scale Totally Nonnegative Least Squares Problems," Journal of Optimization Theory and Applications, Springer, vol. 126(1), pages 191-202, July.
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Cited by:
- Rok Hribar & Timotej Hrga & Gregor Papa & Gašper Petelin & Janez Povh & Nataša Pržulj & Vida Vukašinović, 2022. "Four algorithms to solve symmetric multi-type non-negative matrix tri-factorization problem," Journal of Global Optimization, Springer, vol. 82(2), pages 283-312, February.
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