shimo-lab / universal-geometry-with-ica Goto Github PK

https://github.com/shimo-lab/Universal-Geometry-with-ICA/blob/92a1c4fd628f2c9457df710b461370fa1ecdcc65/universal/src/crosslingual_save_pca_and_ica_embeddings.py#L254C1-L257C30

ica = FastICA(**ica_params)
ica.fit(pca_embed)
R = ica.mixing_
ica_embed = pca_embed @ R

Hi, authors. Thank you for your work. I would like to ask why here using "ica.mixing_", rather than "ica.components_"? I think pca_embed @ ica.components_ is to transform the original data space into a more independent one and it is equal to "ica.fit_transform". I don't know if I am right when I am reading this sklearn guidance.

Hi, thank you for your work! Could you release your code?