CITATION.cff

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title: >-
  An optimal pairwise merge algorithm improves the quality
  and consistency of nonnegative 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/2408.09013'
    description: The ArXiv deposit of the encompassing paper
  - type: doi
    value: 10.48550/arXiv.2408.09013
repository-code: 'https://github.com/HolyLab/NMFMerge.jl'
abstract: >-
  Non-negative matrix factorization (NMF) is a key technique
  for feature extraction and widely used in source
  separation. However, existing algorithms may converge to
  poor local minima, or to one of several minima with
  similar objective value but differing feature
  parametrizations. Additionally, the performance of NMF
  greatly depends on the number of components, but choosing
  the optimal count remains a challenge. Here we show that
  some of these weaknesses may be mitigated by performing
  NMF in a higher-dimensional feature space and then
  iteratively combining components with an
  analytically-solvable pairwise merge strategy.
  Experimental results demonstrate our method helps NMF
  achieve better local optima and greater consistency of the
  solutions. Iterative merging also provides an efficient
  and informative framework for choosing the number of
  components. Surprisingly, despite these extra steps, our
  approach often improves computational performance by
  reducing the occurrence of ``convergence stalling'' near
  saddle points. This can be recommended as a preferred
  approach for most applications of NMF.