# Sparse Gaussian ICA

arXiv: https://arxiv.org/abs/1804.00408 · HTML: https://mlsanity.com/paper/1804.00408/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [On the Identifiability of Sparse ICA without Assuming Non-Gaussianity](https://mlsanity.com/paper/2408.10353/) (2024)
- [DSReg: Provably Recovering Individual World Latents without Reconstruction](https://mlsanity.com/paper/2610.09457/) (2026)

## Abstract (arXiv)

Independent component analysis (ICA) is a cornerstone of modern data analysis. Its goal is to recover a latent random vector S with independent components from samples of X=AS where A is an unknown mixing matrix. Critically, all existing methods for ICA rely on and exploit strongly the assumption that S is not Gaussian as otherwise A becomes unidentifiable. In this paper, we show that in fact one can handle the case of Gaussian components by imposing structure on the matrix A. Specifically, we assume that A is sparse and generic in the sense that it is generated from a sparse Bernoulli-Gaussian ensemble. Under this condition, we give an efficient algorithm to recover the columns of A given only the covariance matrix of X as input even when S has several Gaussian components.