{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/trace-your-sources-in-large-scale-data-one","title":"Trace your sources in large-scale data: one ring to find them all","arxiv_id":"1803.08882","date":"2018-03-23","proceeding":null,"authors":["Alexander Böttcher","Wieland Brendel","Bernhard Englitz","Matthias Bethge"],"abstract":"An important preprocessing step in most data analysis pipelines aims to\nextract a small set of sources that explain most of the data. Currently used\nalgorithms for blind source separation (BSS), however, often fail to extract\nthe desired sources and need extensive cross-validation. In contrast, their\nrarely used probabilistic counterparts can get away with little\ncross-validation and are more accurate and reliable but no simple and scalable\nimplementations are available. Here we present a novel probabilistic BSS\nframework (DECOMPOSE) that can be flexibly adjusted to the data, is extensible\nand easy to use, adapts to individual sources and handles large-scale data\nthrough algorithmic efficiency. DECOMPOSE encompasses and generalises many\ntraditional BSS algorithms such as PCA, ICA and NMF and we demonstrate\nsubstantial improvements in accuracy and robustness on artificial and real\ndata.","url_abs":"http://arxiv.org/abs/1803.08882v1","url_pdf":"http://arxiv.org/pdf/1803.08882v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"trace-your-sources-in-large-scale-data-one","repo_url":"https://github.com/bethgelab/decompose","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"blind-source-separation","task_name":"blind source separation"}],"methods":[{"method_slug":"ica","method_name":"ICA"},{"method_slug":"pca","method_name":"PCA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}