R: Multi-Way Component Analysis

mwTensor-package {mwTensor}

R Documentation

Multi-Way Component Analysis

Description

For single tensor data, any matrix factorization method can be specified the matricised tensor in each dimension by Multi-way Component Analysis (MWCA). An originally extended MWCA is also implemented to specify and decompose multiple matrices and tensors simultaneously (CoupledMWCA). See the reference section of GitHub README.md <https://github.com/rikenbit/mwTensor>, for details of the methods.

Details

The DESCRIPTION file:

Package:	mwTensor
Type:	Package
Title:	Multi-Way Component Analysis
Version:	1.1.0
Authors@R:	c(person("Koki", "Tsuyuzaki", role = c("aut", "cre"), email = "k.t.the-answer@hotmail.co.jp"))
Suggests:	testthat
Depends:	R (>= 4.1.0)
Imports:	methods, MASS, rTensor, nnTensor, ccTensor, iTensor, igraph
Description:	For single tensor data, any matrix factorization method can be specified the matricised tensor in each dimension by Multi-way Component Analysis (MWCA). An originally extended MWCA is also implemented to specify and decompose multiple matrices and tensors simultaneously (CoupledMWCA). See the reference section of GitHub README.md <https://github.com/rikenbit/mwTensor>, for details of the methods.
License:	MIT + file LICENSE
URL:	https://github.com/rikenbit/mwTensor
Author:	Koki Tsuyuzaki [aut, cre]
Maintainer:	Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>

Index of help topics:

CoupledMWCA             Coupled Multi-way Component Analysis
                        (CoupledMWCA)
CoupledMWCAParams-class
                        Class "CoupledMWCAParams"
CoupledMWCAResult-class
                        Class "CoupledMWCAResult"
MWCA                    Multi-way Component Analysis (MWCA)
MWCAParams-class        Class "MWCAParams"
MWCAResult-class        Class "MWCAResult"
defaultCoupledMWCAParams
                        Default parameters for CoupledMWCA
defaultMWCAParams       Default parameters for MWCA
mwTensor-package        Multi-Way Component Analysis
myALS_SVD               Alternating Least Square Singular Value
                        Decomposition (ALS-SVD) as an example of
                        user-defined matrix decomposition.
myCX                    CX Decomposition as an example of user-defined
                        matrix decomposition.
myICA                   Independent Component Analysis (ICA) as an
                        example of user-defined matrix decomposition.
myNMF                   Independent Component Analysis (ICA) as an
                        example of user-defined matrix decomposition.
mySVD                   Singular Value Decomposition (SVD) as an
                        example of user-defined matrix decomposition.
plotTensor3Ds           Plot function for visualization of tensor data
                        structure
toyModel                Toy model of coupled tensor data

Author(s)

Maintainer: NA

References

Andrzej Cichocki et al., (2016). Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions

Andrzej Cichocki et al., (2015). Tensor Decompositions for Signal Processing Applications, IEEE SIGNAL PROCESSING MAGAZINE

Gene H. Golub et al., (2012). Matrix Computation (Johns Hopkins Studies in the Mathematical Sciences), Johns Hopkins University Press

Madeleine Udell et al., (2016). Generalized Low Rank Models, Foundations and Trends in Machine Learning, 9(1).

Andrzej CICHOCK, et. al., (2009). Nonnegative Matrix and Tensor Factorizations.

A. Hyvarinen. (1999). Fast and Robust Fixed-Point Algorithms for Independent Component Analysis. IEEE Transactions on Neural Networks, 10(3), 626-634.

Petros Drineas et al., (2008). Relative-Error CUR Matrix Decompositions, SIAM Journal on Matrix Analysis and Applications, 30(2), 844-881.

Examples

ls("package:mwTensor")