Sparse and Non-Negative BSS for Noisy Data

@article{Rapin2013SparseAN,
  title={Sparse and Non-Negative BSS for Noisy Data},
  author={J. Rapin and J. Bobin and A. Larue and Jean-Luc Starck},
  journal={IEEE Transactions on Signal Processing},
  year={2013},
  volume={61},
  pages={5620-5632}
}
Non-negative blind source separation (BSS) has raised interest in various fields of research, as testified by the wide literature on the topic of non-negative matrix factorization (NMF). In this context, it is fundamental that the sources to be estimated present some diversity in order to be efficiently retrieved. Sparsity is known to enhance such contrast between the sources while producing very robust approaches, especially to noise. In this paper, we introduce a new algorithm in order to… Expand
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References

SHOWING 1-10 OF 59 REFERENCES
Blind Source Separation: the Sparsity Revolution
Sparsity and Morphological Diversity in Blind Source Separation
Csiszár's Divergences for Non-negative Matrix Factorization: Family of New Algorithms
Regularized Alternating Least Squares Algorithms for Non-negative Matrix/Tensor Factorization
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Sparse coding and NMF
  • J. Eggert, E. Korner
  • Mathematics
  • 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)
  • 2004
Hierarchical ALS Algorithms for Nonnegative Matrix and 3D Tensor Factorization
Nonnegative matrix factorization with constrained second-order optimization
...
1
2
3
4
5
...