nnTensor

nnTensor: An R package for non-negative matrix/tensor decomposition - Published in JOSS (2023)

https://github.com/rikenbit/nntensor

Science Score: 93.0%

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Engineering Computer Science - 40% confidence
Last synced: 6 months ago · JSON representation

Repository

R package for Non-negative Tensor Decomposition

Basic Info
  • Host: GitHub
  • Owner: rikenbit
  • License: other
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 745 KB
Statistics
  • Stars: 19
  • Watchers: 12
  • Forks: 5
  • Open Issues: 0
  • Releases: 6
Created over 7 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Contributing License Code of conduct

README.md

DOI CRAN_Status_Badge Downloads Total Downloads :name status badge :registry status badge :total status badge nnTensor status badge GitHub Actions status

nnTensor

R package for Non-negative Tensor Decomposition

Installation

~~~~ git clone https://github.com/rikenbit/nnTensor/ R CMD INSTALL nnTensor ~~~~ or type the code below in the R console window ~~~~ library(devtools) devtools::install_github("rikenbit/nnTensor") ~~~~

References

  • Non-negative Matrix Factorization (NMF)
    • Lee, D. and Seung, H. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)
    • Cichocki, A. et al., Nonnegative Matrix and Tensor Factorizations, Wiley, 2009
    • Kimura, K. A Study on Efficient Algorithms for Nonnegative Matrix/Tensor Factorization, Ph.D. Thesis, 2017
  • Projective NMF/Nonnegative Hebbian Rule (NHR)/Ding-Ti-Peg-Park (DTPP) algorithm/(Column vector-wise) Orthogonal NMF
    • Choi, S. Algorithms for Orthogonal Nonnegative Matrix Factorization, IEEE World Congress on Computational Intelligence, 1828-1832, 2008
  • (Column vector-wise) Orthogonality-regularized NMF
    • Stražar, M. et al., Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins, Bioinformatics, 32(10), 1527-35, 2016
  • 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
  • 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
  • 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
  • Rank estimation of NMF
    • Brunet, J.-P. et al., Metagenes and molecular pattern discovery using matrix factorization. PNAS, 101(12), 4164-4169, 2004
    • Han, X. Cancer Molecular Pattern Discovery by Subspace Consensus Kernel Classification. CSB 2007, 6, 55-65, 2007
    • Frigyesi, A. et al., Non-Negative Matrix Factorization for the Analysis of Complex Gene Expression Data: Identification of Clinically Relevant Tumor Subtypes. Cancer Informatics, 2008
    • Park, H. et al., Lecture 3: Nonnegative Matrix Factorization: Algorithms and Applications. SIAM Gene Golub Summer School, 2019
    • Shao, C. et al., Robust classification of single-cell transcriptome data by nonnegative matrix factorization. Bioinformatics, 33(2), 235-242, 2017
    • Fogel, P., Permuted NMF: A Simple Algorithm Intended to Minimize the Volume of the Score Matrix, arXiv, 2013
    • Kim, P. M. et al., Subsystem Identification Through Dimensionality Reduction of Large-Scale Gene Expression Data. Genome Research, 13(7), 1706-1718, 2003
    • Hutchins, L. N. et al., Position-dependent motif characterization using non-negative matrix factorization. Bioinformatics, 24(23), 2684-2690, 2008
    • Hoyer, P. O., Non-negative Matrix Factorization with Sparseness Constraints. JMLR 5, 1457-1469, 2004
    • Fujita, N. et al., Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses, Scientific Report, 8(1), 9743, 2018
    • Owen, A. B. et al., Bi-Cross-Validation of the SVD and the Nonnegative Matrix Factorization. The Annals of Applied Statistics, 3(2), 564-594, 2009
  • Exponent term depending on Beta parameter
    • Nakano, M. et al., Convergence-guaranteed multiplicative algorithms for nonnegative matrix factorization with Beta-divergence. IEEE MLSP, 283-288, 2010

Contributing

If you have suggestions for how nnTensor 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.

Authors

  • Koki Tsuyuzaki
  • Manabu Ishii
  • Itoshi Nikaido

Owner

  • Name: RIKEN BiT
  • Login: rikenbit
  • Kind: organization
  • Location: Japan

Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research

JOSS Publication

nnTensor: An R package for non-negative matrix/tensor decomposition
Published
April 23, 2023
Volume 8, Issue 84, Page 5015
Authors
Koki Tsuyuzaki ORCID
Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research, Japan, Japan Science and Technology Agency, PRESTO, Japan
Itoshi Nikaido ORCID
Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research, Japan, Science Innovation (Bioinformatics), Degree Programs in Systems and Information Engineering, Graduate School of Science and Technology), University of Tsukuba, Japan, Department of Functional Genome Informatics, Division of Biological Data Science, Medical Research Institute, Tokyo Medical and Dental University, Japan
Editor
Prashant Jha ORCID
Tags
non-negative matrix factorization non-negative tensor decomposition dimension reduction

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Packages

  • Total packages: 2
  • Total downloads:
    • cran 441 last-month
  • Total docker downloads: 1,304
  • Total dependent packages: 4
    (may contain duplicates)
  • Total dependent repositories: 5
    (may contain duplicates)
  • Total versions: 34
  • Total maintainers: 1
cran.r-project.org: nnTensor

Non-Negative Tensor Decomposition

  • Versions: 21
  • Dependent Packages: 4
  • Dependent Repositories: 5
  • Downloads: 441 Last month
  • Docker Downloads: 1,304
Rankings
Dependent packages count: 9.3%
Forks count: 10.8%
Dependent repos count: 13.0%
Average: 14.6%
Stargazers count: 15.1%
Downloads: 16.9%
Docker downloads count: 22.7%
Last synced: 6 months ago
conda-forge.org: r-nntensor
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  • Dependent Repositories: 0
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Dependent repos count: 34.0%
Average: 45.9%
Stargazers count: 48.9%
Forks count: 49.6%
Dependent packages count: 51.2%
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Dependencies

DESCRIPTION cran
  • R >= 3.4.0 depends
  • MASS * imports
  • fields * imports
  • ggplot2 * imports
  • methods * imports
  • plot3D * imports
  • rTensor * imports
  • tagcloud * imports
  • testthat * suggests
.github/workflows/build_test_push.yml actions
  • actions/checkout v2 composite
  • docker/build-push-action v1 composite
  • docker/login-action v1 composite
Dockerfile docker
  • bioconductor/bioconductor_docker devel build