Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent

This repository provides a Python library that implements the Elastic Boolean Matrix Factorization (Elbmf) algorithm using PyTorch. It provides an efficient methods for factorizing binary matrices into low-rank matrices using a continuous relaxation, an elastic-net inspired Boolean regularization, and proximal gradient descent.

Acknowledgments

This project is based on the research paper 'Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent'

@inproceedings{dalleiger2022efficiently,
title={Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent},
author={Sebastian Dalleiger and Jilles Vreeken},
booktitle={Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS)},
year={2022}
}

Installation

pip install torch
pip install git+https://github.com/sdall/elbmf-python

Usage

importtorchfromelbmfimportelbmfX=torch.randint(0, 2, (100, 100))
U, V=elbmf(
X=X, # a Boolean n*m matrix n_components=20, # number of componentsl1reg=0.01, # l1 coefficientl2reg=0.02, # l2 coefficientregularization_rate=lambdat: 1.02**t, # monotonically increasing regularization-rate functionmaxiter=3000, # maximum number of iterationstolerance=1e-8, # the threshold to the absolute difference between the current and previous losses determines the convergencebeta=0.0001, # inertial coefficient of iPALMcallback=None, # e.g. lambda t, U, V, fn: print(t, fn)with_rounding=True) # rounds U and V in case of early stopping

If the resulting reconstruction is unexpected or not ideal, you might want to try to tweak l1reg, l2reg, maxiter, and most importantly the regularization_rate, with disabled with_rounding (for debugging purposes) and enabled callback.

Contributing

Contributions to Elbmf are welcome. If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the GitHub repository: https://github.com/sdall/elbmf-python

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

If you have any questions or inquiries, please contact sdalleig@mpi-inf.mpg.de.

Feel free to reach out with any feedback or suggestions.