A set of matrix decomposition algorithms implemented as PyTorch classes
Because PyTorchDecomp is a PyPI package, please install it by pip command as follows:
python -m venv env
pip install torchdecompFor the other OS-specific or package-manager-specific installation, please check the README.md of PyTorch.
See the tutorials.
- 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
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.
PyTorchDecomp has a MIT license, as found in the LICENSE file.
- Koki Tsuyuzaki