Two to Five Truths in Non-negative Matrix Factorization

Author:

Conroy John M.,Molino Neil,Baughman Brian,Gomez Rod,Kaliszewski Ryan,Lines Nicholas A.

Publisher

Springer Nature Switzerland

Reference19 articles.

1. Arora, S., et al.: Learning topic models - provably and efficiently. Commun. ACM 61(4), 85–93 (2018)

2. Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3(null), 1137–1155 (2003)

3. Donoho, D., Stodden, V.: When does non-negative matrix factorization give correct decomposition into parts? In: Advances in Neural Information Processing Systems, vol. 16, p. 03 (2004)

4. Gillis, N., Vavasis, S.: Semidefinite programming based preconditioning for more robust near-separable nonnegative matrix factorization. SIAM J. Optim. 25, 10 (2013)

5. Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)

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