GSVD-NMF: Recovering Missing Features in Non-negative Matrix Factorization
<|reference_start|>GSVD-NMF: Recovering Missing Features in Non-negative Matrix Factorization: Non-negative matrix factorization (NMF) is an important tool in signal processing and widely used to separate mixed sources into their components. However, NMF is NP-hard and thus may fail to discover the ideal factorization; moreover, the number of components may not be known in advance and thus features may be missed or incompletely separated. To recover missing components from under-complete NMF, we introduce GSVD-NMF, which proposes new components based on the generalized singular value decomposition (GSVD) between preliminary NMF results and the SVD of the original matrix. Simulation and experimental results demonstrate that GSVD-NMF often recovers missing features from under-complete NMF and helps NMF achieve better local optima.<|reference_end|>
@article{guo2024gsvd-nmf:,
    title={GSVD-NMF: Recovering Missing Features in Non-negative Matrix
  Factorization},
    author={Youdong Guo, Timothy E. Holy},
    journal={arXiv preprint arXiv:2408.08260},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.08260},
    primaryClass={cs.LG eess.SP}
}