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.

Authors

• Koki Tsuyuzaki