R: Discrete Matrix/Tensor Decomposition

dcTensor-package {dcTensor}

R Documentation

Discrete Matrix/Tensor Decomposition

Description

Semi-Binary and Semi-Ternary Matrix Decomposition are performed based on Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/dcTensor>.

Details

The DESCRIPTION file:

Package:	dcTensor
Type:	Package
Title:	Discrete Matrix/Tensor Decomposition
Version:	1.3.0
Authors@R:	c(person("Koki", "Tsuyuzaki", role = c("aut", "cre"), email = "k.t.the-answer@hotmail.co.jp"))
Depends:	R (>= 3.4.0)
Imports:	methods, MASS, fields, rTensor, nnTensor
Suggests:	knitr, rmarkdown, testthat
Description:	Semi-Binary and Semi-Ternary Matrix Decomposition are performed based on Non-negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD). For the details of the methods, see the reference section of GitHub README.md <https://github.com/rikenbit/dcTensor>.
License:	MIT + file LICENSE
URL:	https://github.com/rikenbit/dcTensor
VignetteBuilder:	knitr
Author:	Koki Tsuyuzaki [aut, cre]
Maintainer:	Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>

Index of help topics:

dNMF                    Discretized Non-negative Matrix Factorization
                        Algorithms (dNMF)
dNMTF                   Discretized Non-negative Matrix
                        Tri-Factorization Algorithms (dNMTF)
dNTD                    Discretized Non-negative Tucker Decomposition
                        Algorithms (dNTD)
dNTF                    Discretized Non-negative CP Decomposition
                        Algorithms (dNTF)
dPLS                    Discretized Partial Least Squares (dPLS)
dSVD                    Discretized Singular Value Decomposition (dSVD)
dcTensor-package        Discrete Matrix/Tensor Decomposition
djNMF                   Discretized Joint Non-negative Matrix
                        Factorization Algorithms (djNMF)
dsiNMF                  Discretized Simultaneous Non-negative Matrix
                        Factorization Algorithms (dsiNMF)
toyModel                Toy model data for using dNMF, dSVD, dsiNMF,
                        djNMF, dPLS, dNTF, and dNTD

Author(s)

Maintainer: NA

References

Z. Zhang, T. Li, C. Ding and X. Zhang, (2007). Binary Matrix Factorization with Applications, Seventh IEEE International Conference on Data Mining (ICDM 2007), 391-400

Examples

ls("package:dcTensor")