Algorithms and applications for approximate nonnegative matrix factorization

@article{Berry2007AlgorithmsAA,
  title={Algorithms and applications for approximate nonnegative matrix factorization},
  author={Michael W. Berry and Murray Browne and Amy Nicole Langville and Victor Pa{\'u}l Pauca and Robert J. Plemmons},
  journal={Computational Statistics & Data Analysis},
  year={2007},
  volume={52},
  pages={155-173}
}
The development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis are presented. The evolution and convergence properties of hybrid methods based on both sparsity and smoothness constraints for the resulting nonnegative matrix factors are discussed. The interpretability of NMF outputs in specific contexts are provided along with opportunities for future work in the… CONTINUE READING

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