DBNMFrank: Rank Selection for Non-Negative Matrix Factorization

Given the non-negative data and its distribution, the package estimates the rank parameter for Non-negative Matrix Factorization. The method is based on hypothesis testing, using a deconvolved bootstrap distribution to assess the significance level accurately despite the large amount of optimization error. The distribution of the non-negative data can be either Normal distributed or Poisson distributed.

Getting started

Package details

AuthorYun Cai [aut, cre], Hong Gu [aut], Tobias Kenney [aut]
MaintainerYun Cai <Yun.Cai@dal.ca>
LicenseGPL (>= 3)
Package repositoryView on CRAN
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DBNMFrank documentation built on June 3, 2022, 9:07 a.m.