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The nmfbin
R package provides a simple Non-Negative
Matrix Factorization (NMF) implementation tailored for binary data
matrices. It offers a choice of initialization methods, loss functions
and updating algorithms.
NMF is typically used for reducing high-dimensional matrices into lower (k-) rank ones where k is chosen by the user. Given a non-negative matrix X of size \(m \times n\), NMF looks for two non-negative matrices W (\(m \times k\)) and H (\(k \times n\)), such that:
\[X \approx W \times H\]
In topic modelling, W is interpreted as the document-topic matrix and H as the topic-feature matrix.
Unlike most other NMF packages, nmfbin
is focused on
binary (Boolean) data, while keeping the number of dependencies to a
minimum. For more information see the website.
You can install the development version of nmfbin
from
GitHub with:
# install.packages("remotes")
::install_github("michalovadek/nmfbin") remotes
The input matrix can only contain 0s and 1s.
# load
library(nmfbin)
# Create a binary matrix for demonstration
<- matrix(sample(c(0, 1), 100, replace = TRUE), ncol = 10)
X
# Perform Logistic NMF
<- nmfbin(X, k = 3, optimizer = "mur", init = "nndsvd", max_iter = 1000) results
@Manual{,
title = {nmfbin: Non-Negative Matrix Factorization for Binary Data},
author = {Michal Ovadek},
year = {2023},
note = {R package version 0.2.1},
url = {https://michalovadek.github.io/nmfbin/},
}
Contributions to the nmfbin
package are more than
welcome. Please submit pull requests or open an issue for
discussion.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.