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Showing content from https://github.com/rikenbit/dcTensor below:

rikenbit/dcTensor: R package for Discrete Matrix/Tensor Decomposition

dcTensor is an R package for Discrete Matrix/Tensor Decomposition. dcTensor provides the discretized version of matrix and tensor decomposition algorithms such as:

• Discretized Non-negative Matrix Factorization Algorithms (dNMF)
• Discretized Non-negative Matrix Tri-Factorization Algorithms (dNMTF)
• Discretized Singular Value Decomposition (dSVD)
• Discretized Simultaneous Non-negative Matrix Factorization Algorithms (dsiNMF)
• Discretized Joint Non-negative Matrix Factorization Algorithms (djNMF)
• Discretized Partial Least Squares (dPLS)
• Discretized Non-negative CP Decomposition Algorithms (dNTF)
• Discretized Non-negative Tucker Decomposition Algorithms (dNTD)

Here "discretized" means that the factor matrices extracted from the data are estimated with discretizing regularization, so that the values are binary (e.g., {0,1}) or ternary (e.g., {0,1,2}), as much as possible. Binary data analysis is recently featured in some data science domains such as market basket data, document-term data, Web click-stream data, DNA microarray expression profiles, or protein-protein complex interaction networks.

To install dcTensor from CRAN, type as follows:

install.packages("dcTensor")

To install the latest dcTensor from GitHub, type as follows:

git clone https://github.com/rikenbit/dcTensor/
R CMD INSTALL dcTensor

or type the code below in the R console window

library(devtools)
devtools::install_github("rikenbit/dcTensor")

For the details of dcTensor's functions, see the help page of each function as follows.

library("dcTensor")

?toyModel
?dNMF
?dNMTF
?dSVD
?dsiNMF
?djNMF
?dPLS
?dNTF
?dNTD

• Binary Matrix Factorization (BMF)
  • Z. Zhang, T. Li, C. Ding and X. Zhang, "Binary Matrix Factorization with Applications," Seventh IEEE International Conference on Data Mining (ICDM 2007), Omaha, NE, USA, 2007, pp. 391-400, doi: 10.1109/ICDM.2007.99.
• Non-negative Matrix Tri-Factorization (NMTF)
  • Copar, A. et al., Fast Optimization of Non-Negative Matrix Tri-Factorization: Supporting Information, PLOS ONE, 14(6), e0217994, 2019
  • Long, B. et al., Co-clustering by Block Value Decomposition, SIGKDD'05, 635–640, 2005
  • Ding, C. et al., Orthogonal Nonnegative Matrix Tri-Factorizations for Clustering, 12th ACM SIGKDD'06, 126–135, 2006
• Singular Value Decomposition (SVD) based on Gradient Descent
  • Tsuyuzaki K, et al., Benchmarking principal component analysis for large-scale single-cell RNA-sequencing. BMC Genome Biology. 21(1), 9, 2020
• Simultaneous Non-negative Matrix Factorization (siNMF)
  • Badea, L. Extracting Gene Expression Profiles Common to Colon and Pancreatic Adenocarcinoma using Simultaneous nonnegative matrix factorization, Pacific Symposium on Biocomputing, 279-290, 2008
  • Zhang, S. et al., Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Research, 40(19), 9379-9391, 2012
  • Yilmaz, Y. K. et al., Probabilistic Latent Tensor Factorization, IVA/ICA 2010, 346-353, 2010
• Joint Non-negative Matrix Factorization (jNMF)
  • Zi, Yang, et al., A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data, Bioinformatics, 32(1), 1-8, 2016
• Partial Least Squares (PLS) based on Gradient Descent
  • Arora, R. et al., Stochastic Optimization for PCA and PLS, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 861-868, 2012
• Non-negative CP Decomposition (NTF)
  • α-Divergence (KL, Pearson, Hellinger, Neyman) / β-Divergence (KL, Frobenius, IS)
    • Cichocki, A. et al., Non-negative Tensor Factorization using Alpha and Beta Divergence, ICASSP '07, III-1393-III-1396, 2007
    • mathieubray/TensorKPD.R
  • Fast HALS
    • Phan, A. H. et al., Multi-way Nonnegative Tensor Factorization Using Fast Hierarchical Alternating Least Squares Algorithm (HALS), NOLTA 2008, 2008
  • α-HALS/β-HALS
    • Cichocki, A. et al., Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations, IEICE Transactions, 92-A, 708-721, 2009
• Non-negative Tucker Decomposition (NTD)
  • Frobenius/KL
    • Kim, Y.-D. et al., Nonnegative Tucker Decomposition, IEEE CVPR, 1-8, 2007
  • α-Divergence (KL, Pearson, Hellinger, Neyman) / β-Divergence (KL, Frobenius, IS)
    • Kim, Y.-D. et al., Nonneegative Tucker Decomposition with Alpha-Divergence, 2008
    • Phan, A. H. et al., Fast and efficient algorithms for nonnegative Tucker decomposition, ISNN 2008, 772-782, 2008
  • Fast HALS
    • Phan, A. H. et al., Extended HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification, Neurocomputing, 74(11), 1956-1969, 2011

If you have suggestions for how dcTensor could be improved, or want to report a bug, open an issue! We'd love all and any contributions.

For more, check out the Contributing Guide.

• Koki Tsuyuzaki

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