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      "page": "HALSacc",
      "title": "Accelerated hierarchical alternating least squares NMF. For a reference to the method, see N. Gillis, Nonnegative matrix factorization: complexity, algorithms and applications [Section 4.2, Algo. 6], PhD thesis, Université catholique de Louvain, February 2011.",
      "topics": [
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      ]
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      "page": "hNMF",
      "title": "Hierarchical non-negative matrix factorization.",
      "topics": [
        "hNMF"
      ]
    },
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      "page": "imoverlay",
      "title": "Overlay a mask or a color scaled image on top of a background image",
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      ]
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      "page": "initializeNMF",
      "title": "Initialize NMF model with initial spectral data",
      "topics": [
        "initializeNMF"
      ]
    },
    {
      "page": "initializeSPA",
      "title": "The successive projection algorithm, a useful method for initializing the NMF source matrix",
      "topics": [
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    },
    {
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      "title": "Perform Non-Negative Matrix factorization",
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      ]
    },
    {
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      "title": "NMF by alternating non-negative least squares using projected gradients.  For a reference to the method, see C.-J. Lin, \"Projected Gradient Methods for Non-negative Matrix Factorization\", Neural computation 19.10 (2007): 2756-2779.",
      "topics": [
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      ]
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    {
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    {
      "page": "residualNMF",
      "title": "Computation of relative NMF residual per observation",
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      ]
    },
    {
      "page": "scaleNMFResult",
      "title": "Apply fixed scaling to NMF model matrices by normalizing the basis vectors",
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        "scaleNMFResult"
      ]
    },
    {
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      "title": "Semi-NMF based on multiplicative update rules. Reference: C. Ding, T. Li, and M.I. Jordan, \"Convex and semi-nonnegative matrix factorizations\", IEEE Transations on Pattern Analysis and Machine Intelligence, vol. 32, no. 1, pp. 45-55, 2010.",
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