Description Usage Arguments Value Author(s)

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.

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`X` |
Input data matrix, each column represents one data point and the rows correspond to the different features |

`nmfMod` |
Valid NMF model, containing initialized factor matrices (in accordance with the NMF package definition) |

`tol` |
Tolerance for a relative stopping condition |

`maxIter` |
Maximum number of iterations |

`timeLimit` |
Limit of time duration NMF analysis |

`checkDivergence` |
Boolean indicating whether divergence checking should be performed Default is TRUE, but it should be set to FALSE when using random initialization |

Resulting NMF model (in accordance with the NMF package definition)

nsauwen

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