Back Factorització matricial no negativa Catalan Nichtnegative Matrixfaktorisierung German Factorización no negativa de matrices Spanish فاکتورگیری نامنفی ماتریس Persian 비음수 행렬 분해 Korean Неотрицательное матричное разложение Russian Розклад невід'ємних матриць Ukrainian
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Non-negative matrix factorization (NMF or NNMF), also non-negative matrix approximation[1][2] is a group of algorithms in multivariate analysis and linear algebra where a matrix V is factorized into (usually) two matrices W and H, with the property that all three matrices have no negative elements. This non-negativity makes the resulting matrices easier to inspect. Also, in applications such as processing of audio spectrograms or muscular activity, non-negativity is inherent to the data being considered. Since the problem is not exactly solvable in general, it is commonly approximated numerically.
NMF finds applications in such fields as astronomy,[3][4] computer vision, document clustering,[1] missing data imputation,[5] chemometrics, audio signal processing, recommender systems,[6][7] and bioinformatics.[8]
- ^ a b Cite error: The named reference
dhillonwas invoked but never defined (see the help page). - ^ Tandon, Rashish; Sra, Suvrit (September 13, 2010). Sparse nonnegative matrix approximation: new formulations and algorithms (PDF) (Report). Max Planck Institute for Biological Cybernetics. Technical Report No. 193.
- ^ Cite error: The named reference
blantonRoweis07was invoked but never defined (see the help page). - ^ Cite error: The named reference
ren18was invoked but never defined (see the help page). - ^ Ren, Bin; Pueyo, Laurent; Chen, Christine; Choquet, Elodie; Debes, John H; Duechene, Gaspard; Menard, Francois; Perrin, Marshall D. (2020). "Using Data Imputation for Signal Separation in High Contrast Imaging". The Astrophysical Journal. 892 (2): 74. arXiv:2001.00563. Bibcode:2020ApJ...892...74R. doi:10.3847/1538-4357/ab7024. S2CID 209531731.
- ^ Rainer Gemulla; Erik Nijkamp; Peter J. Haas; Yannis Sismanis (2011). Large-scale matrix factorization with distributed stochastic gradient descent. Proc. ACM SIGKDD Int'l Conf. on Knowledge discovery and data mining. pp. 69–77.
- ^ Yang Bao; et al. (2014). TopicMF: Simultaneously Exploiting Ratings and Reviews for Recommendation. AAAI.
- ^ Ben Murrell; et al. (2011). "Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution". PLOS ONE. 6 (12): e28898. Bibcode:2011PLoSO...628898M. doi:10.1371/journal.pone.0028898. PMC 3245233. PMID 22216138.