PyTorchDecomp: A set of matrix decomposition models implemented as PyTorch classes
PyTorchDecomp
A set of matrix decomposition algorithms implemented as PyTorch classes
Installation
Because PyTorchDecomp is a PyPI package, please install it by pip command as follows:
python -m venv env
pip install torchdecomp
For the other OS-specific or package-manager-specific installation, please check the README.md of PyTorch.
Usage
See the tutorials.
References
LU/QR/Cholesky/Eigenvalue Decomposition
Gene H. Golub, Charles F. Van Loan Matrix Computations (Johns Hopkins Studies in the Mathematical Sciences)
Principal Component Analysis (PCA) / Partial Least Squares (PLS)
R. Arora, A. Cotter, K. Livescu and N. Srebro, Stochastic optimization for PCA and PLS, 2012 50th Annual Allerton Conference on Communication, Control, and Computing, 2012, 861-868. 2012
Independent Component Analysis (ICA)
Hybarinen, A. and Oja, E. Independent component analysis: algorithms and applications, Neural Networks, 13, 411-430. 2000
Deep Deterministic ICA (DDICA)
H. Li, S. Yu and J. C. Príncipe, Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3878-3882, 2022
Non-negative Matrix Factorization (NMF)
Kimura, K. A Study on Efficient Algorithms for Nonnegative Matrix/Tensor Factorization, Ph.D. Thesis, 2017
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
Beta-divergence NMF and Backpropagation
https://yoyololicon.github.io/posts/2021/02/torchnmf-algorithm/
Contributing
If you have suggestions for how PyTorchDecomp 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.
License
PyTorchDecomp has a MIT license, as found in the LICENSE file.