csianglim / nnTensor

R package for Non-negative Tensor Decomposition

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) : Nonnegative Matrix and Tensor Factorizations, Andrzej CICHOCK, et. al., 2009, A Study on Efficient Algorithms for Nonnegative Matrix/Tensor Factorization, Keigo Kimura, 2017
• Projected NMF
• Nonnegative Hebbian Rule (NHR)
• Ding-Ti-Peg-Park (DTPP) algorithm
• (Column vector-wise) Orthogonal NMF
  • Algorithms for Orthogonal Nonnegative Matrix Factorization, Seungjin Choi, 2008
• (Column vector-wise) Orthogonality-regularized NMF
  • Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins, Martin Stražar, Marinka Žitnik, Blaž Zupan, Jernej Ule, Tomaž Curk, Bioinformatics, 15;32(10):1527-35, 2016
• Non-negative Matrix Tri-Factorization (NMTF) : Fast Optimization of Non-Negative Matrix Tri-Factorization: Supporting Information, Andrej Copar, et. al., PLOS ONE, 14(6), e0217994, 2019, Co-clustering by Block Value Decomposition, Bo Long et al., SIGKDD'05, 2005, Orthogonal Nonnegative Matrix Tri-Factorizations for Clustering, Chris Ding et. al., 12th ACM SIGKDD, 2006
• Simultaneous Non-negative Matrix Factorization (siNMF) : Extracting Gene Expression Profiles Common to Colon and Pancreatic Adenocarcinoma using Simultaneous nonnegative matrix factorization, Liviu Badea, Pacific Symposium on Biocomputing, 13:279-290, 2009, Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Shihua Zhang, et al., Nucleic Acids Research, 40(19), 9379-9391, 2012, Probabilistic Latent Tensor Factorization, International Conference on Latent Variable Analysis and Signal Separation, Y. Kenan Yilmaz et al., 346-353, 2010
• Joint Non-negative Matrix Factorization (jNMF) : A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data, Zi Yang, et al., Bioinformatics, 32(1), 1-8, 2016
• Non-negative CP Decomposition (NTF)
  • α-Divergence (KL, Pearson, Hellinger, Neyman) / β-Divergence (KL, Frobenius, IS) : Non-negative Tensor Factorization using Alpha and Beta Divergence, Andrzej CICHOCKI et. al., 2007, TensorKPD.R (gist of mathieubray)
  • Fast HALS : Multi-way Nonnegative Tensor Factorization Using Fast Hierarchical Alternating Least Squares Algorithm (HALS), Anh Huy PHAN et. al., 2008
  • α-HALS/β-HALS : Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations, Andrzej CICHOCKI et. al., 2008
• Non-negative Tucker Decomposition (NTD)
  • KL, Frobenius : Nonnegative Tucker Decomposition, Yong-Deok Kim et. al., 2007
  • α-Divergence (KL, Pearson, Hellinger, Neyman) / β-Divergence (KL, Frobenius, IS) : Nonneegative Tucker Decomposition With Alpha-Divergence, Yong-Deok Kim et. al., 2008, Fast and efficient algorithms for nonnegative Tucker decomposition, Anh Huy Phan, 2008
  • Fast HALS : Extended HALS algorithm for nonnegative Tucker decomposition and its applications for multiway analysis and classification, Anh Hyu Phan et. al., 2011
• Rank estimation of NMF
  • Jean-Philippe Brunet. et. al., (2004). Metagenes and molecular pattern discovery using matrix factorization. PNAS
  • Xiaoxu Han. (2007). CANCER MOLECULAR PATTERN DISCOVERY BY SUBSPACE CONSENSUS KERNEL CLASSIFICATION
  • Attila Frigyesi. et. al., (2008). Non-Negative Matrix Factorization for the Analysis of Complex Gene Expression Data: Identification of Clinically Relevant Tumor Subtypes. Cancer Informatics
  • Haesun Park. et. al., (2019). Lecture 3: Nonnegative Matrix Factorization: Algorithms and Applications. SIAM Gene Golub Summer School, Aussois France, June 18, 2019
  • Chunxuan Shao. et. al., (2017). Robust classification of single-cell transcriptome data by nonnegative matrix factorization. Bioinformatics
  • Paul Fogel (2013). Permuted NMF: A Simple Algorithm Intended to Minimize the Volume of the Score Matrix
  • Philip M. Kim. et. al., (2003). Subsystem Identification Through Dimensionality Reduction of Large-Scale Gene Expression Data. Genome Research
  • Lucie N. Hutchins. et. al., (2008). Position-dependent motif characterization using non-negative matrix factorization. Bioinformatics
  • Patrik O. Hoyer (2004). Non-negative Matrix Factorization with Sparseness Constraints. Journal of Machine Learning 5
  • N. Fujita et al., (2018) Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses, Scientific Report
  • Art B. Owen et. al., (2009). Bi-Cross-Validation of the SVD and the Nonnegative Matrix Factorization. The Annals of Applied Statistics
• Exponent term depending on Beta parameter
  • M. Nakano et al., (2010). Convergence-guaranteed multiplicative algorithms for nonnegative matrix factorization with Beta-divergence. IEEE Workshop on Machine Learning for Signal Processing

License

Copyright (c) 2018 Koki Tsuyuzaki and Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Reseach Released under the Artistic License 2.0.

Authors

• Koki Tsuyuzaki
• Manabu Ishii
• Itoshi Nikaido