Biclustering Readings and Manuscripts via Non-negative Matrix Factorization, with Application to the Text of Jude
Machine Learning
2016-02-04 v1
Abstract
The text-critical practice of grouping witnesses into families or texttypes often faces two obstacles: Contamination in the manuscript tradition, and co-dependence in identifying characteristic readings and manuscripts. We introduce non-negative matrix factorization (NMF) as a simple, unsupervised, and efficient way to cluster large numbers of manuscripts and readings simultaneously while summarizing contamination using an easy-to-interpret mixture model. We apply this method to an extensive collation of the New Testament epistle of Jude and show that the resulting clusters correspond to human-identified textual families from existing research.
Keywords
Cite
@article{arxiv.1602.01323,
title = {Biclustering Readings and Manuscripts via Non-negative Matrix Factorization, with Application to the Text of Jude},
author = {Joey McCollum and Stephen Brown},
journal= {arXiv preprint arXiv:1602.01323},
year = {2016}
}
Comments
31 pages, 2 figures, 42 tables