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DBNMFrank (version 0.1.0)

Rank Selection for Non-Negative Matrix Factorization

Description

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.

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Version

Install

install.packages('DBNMFrank')

Monthly Downloads

143

Version

0.1.0

License

GPL (>= 3)

Maintainer

Yun Cai

Last Published

June 3rd, 2022

Functions in DBNMFrank (0.1.0)

DBrank

Rank Selection for Non-Negative Matrix Factorization