Description Usage Arguments Details Value Author(s) References See Also Examples

Spectral biparitioning by rank-2 matrix factorization

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`A` |
matrix of features-by-samples in dense or sparse format (preferred classes are "matrix" or "Matrix::dgCMatrix", respectively). Prefer sparse storage when more than half of all values are zero. |

`tol` |
stopping criteria, the correlation distance between |

`maxit` |
stopping criteria, maximum number of alternating updates of |

`nonneg` |
enforce non-negativity |

`samples` |
samples to include in bipartition, numbered from 1 to |

`seed` |
random seed for model initialization |

`verbose` |
print model tolerances between iterations |

`calc_dist` |
calculate the relative cosine distance of samples within a cluster to either cluster centroid. If |

`diag` |
scale factors in |

Spectral bipartitioning is a popular subroutine in divisive clustering. The sign of the difference between sample loadings in factors of a rank-2 matrix factorization gives a bipartition that is nearly identical to an SVD.

Rank-2 matrix factorization by alternating least squares is faster than rank-2-truncated SVD (i.e. *irlba*).

This function is a specialization of rank-2 `nmf`

with support for factorization of only a subset of samples, and with additional calculations on the factorization model relevant to bipartitioning. See `nmf`

for details regarding rank-2 factorization.

A list giving the bipartition and useful statistics:

v : vector giving difference between sample loadings between factors in a rank-2 factorization

dist : relative cosine distance of samples within a cluster to centroids of assigned vs. not-assigned cluster

size1 : number of samples in first cluster (positive loadings in 'v')

size2 : number of samples in second cluster (negative loadings in 'v')

samples1: indices of samples in first cluster

samples2: indices of samples in second cluster

center1 : mean feature loadings across samples in first cluster

center2 : mean feature loadings across samples in second cluster

Zach DeBruine

Kuang, D, Park, H. (2013). "Fast rank-2 nonnegative matrix factorization for hierarchical document clustering." Proc. 19th ACM SIGKDD intl. conf. on Knowledge discovery and data mining.

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