  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.
