• Corpus ID: 4320823

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

@article{Bttcher2018TraceYS,
  title={Trace your sources in large-scale data: one ring to find them all},
  author={Alexander B{\"o}ttcher and Wieland Brendel and Bernhard Englitz and Matthias Bethge},
  journal={ArXiv},
  year={2018},
  volume={abs/1803.08882}
}

• Alexander Böttcher, Wieland Brendel, +1 author M. Bethge
• Published 23 March 2018
• Computer Science
• 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.