Sparse Independent Component Analysis without Assuming Non-Gaussianity

This repository contains an implementation of the independent component analysis method described in "On the Identifiability of Sparse ICA without Assuming Non-Gaussianity".

If you find it useful, please consider citing:

@inproceedings{ng2023identifiability,
  author = {Ng, Ignavier and Zheng, Yujia and Dong, Xinshuai and Zhang, Kun},
  booktitle = {Advances in Neural Information Processing Systems},
  title = {On the Identifiability of Sparse ICA without Assuming Non-Gaussianity},
  year = {2023}
}

Requirements

• Python 3.6+
• numpy
• scipy

Running the Methods

• To run an example for both decomposition-based and likelihood-based methods, run the following:

python main.py

Acknowledgments

• Parts of the methods and optimization procedure are adapted from notears and notears-convergence.