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.
The DESCRIPTION file:
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Koki Tsuyuzaki [aut, cre]
Maintainer: Koki Tsuyuzaki <email@example.com>
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.
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