# Trace your sources in large-scale data: one ring to find them all

arXiv: https://arxiv.org/abs/1803.08882 · HTML: https://mlsanity.com/paper/1803.08882/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

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

- [Enhancing Deep Learning Models through Tensorization: A Comprehensive Survey and Framework](https://mlsanity.com/paper/2309.02428/) (2023)
- [TGCM: Topic-Guided Consistency Modeling for One-Step Disentanglement of Interleaved APT Technique Sequences](https://mlsanity.com/paper/2606.18651/) (2026)

## Abstract (arXiv)

An important preprocessing step in most data analysis pipelines aims to extract a small set of sources that explain most of the data. Currently used algorithms for blind source separation (BSS), however, often fail to extract the desired sources and need extensive cross-validation. In contrast, their rarely used probabilistic counterparts can get away with little cross-validation and are more accurate and reliable but no simple and scalable implementations are available. Here we present a novel probabilistic BSS framework (DECOMPOSE) that can be flexibly adjusted to the data, is extensible and easy to use, adapts to individual sources and handles large-scale data through algorithmic efficiency. DECOMPOSE encompasses and generalises many traditional BSS algorithms such as PCA, ICA and NMF and we demonstrate substantial improvements in accuracy and robustness on artificial and real data.