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An important preprocessing step in most data analysis pipelines aims toextract a small set of sources that explain most of the data. Currently usedalgorithms for blind source separation (BSS), however, often fail to extractthe desired sources and need extensive cross-validation. In contrast, theirrarely used probabilistic counterparts can get away with littlecross-validation and are more accurate and reliable but no simple and scalableimplementations are available. Here we present a novel probabilistic BSSframework (DECOMPOSE) that can be flexibly adjusted to the data, is extensibleand easy to use, adapts to individual sources and handles large-scale datathrough algorithmic efficiency. DECOMPOSE encompasses and generalises manytraditional BSS algorithms such as PCA, ICA and NMF and we demonstratesubstantial improvements in accuracy and robustness on artificial and realdata.

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