# Gaussian Compression Stream: Principle and Preliminary Results

arXiv: https://arxiv.org/abs/2011.05390 · HTML: https://mlsanity.com/paper/2011.05390/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Learning nonnegative matrix factorizations from compressed data](https://mlsanity.com/paper/2409.04994/) (2024)

## Abstract (arXiv)

Random projections became popular tools to process big data. In particular, when applied to Nonnegative Matrix Factorization (NMF), it was shown that structured random projections were far more efficient than classical strategies based on Gaussian compression. However, they remain costly and might not fully benefit from recent fast random projection techniques. In this paper, we thus investigate an alternative to structured ran-om projections-named Gaussian compression stream-which (i) is based on Gaussian compressions only, (ii) can benefit from the above fast techniques, and (iii) is shown to be well-suited to NMF.