CITATION.cff

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cff-version: 1.2.0
title: 'GSVD-NMF: Recovering Missing Features in Non-negative Matrix Factorization'
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Youdong
    family-names: Guo
    orcid: 'https://orcid.org/0009-0007-7787-3722'
  - given-names: Timothy E.
    family-names: Holy
    orcid: 'https://orcid.org/0000-0002-2429-1071'
identifiers:
  - type: url
    value: 'https://arxiv.org/abs/2408.08260'
    description: The ArXiv deposit of the encompassing paper.
doi: 'https://doi.org/10.48550/arXiv.2408.08260'
repository-code: 'https://github.com/HolyLab/GsvdInitialization.jl'
abstract: >-
  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.