{"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/sparsity-and-adaptivity-for-the-blind","title":"Sparsity and adaptivity for the blind separation of partially correlated sources","arxiv_id":"1412.4005","date":"2014-12-09","proceeding":null,"authors":["Jerome Bobin","Jeremy Rapin","Anthony Larue","Jean-Luc Starck"],"abstract":"Blind source separation (BSS) is a very popular technique to analyze\nmultichannel data. In this context, the data are modeled as the linear\ncombination of sources to be retrieved. For that purpose, standard BSS methods\nall rely on some discrimination principle, whether it is statistical\nindependence or morphological diversity, to distinguish between the sources.\nHowever, dealing with real-world data reveals that such assumptions are rarely\nvalid in practice: the signals of interest are more likely partially\ncorrelated, which generally hampers the performances of standard BSS methods.\nIn this article, we introduce a novel sparsity-enforcing BSS method coined\nAdaptive Morphological Component Analysis (AMCA), which is designed to retrieve\nsparse and partially correlated sources. More precisely, it makes profit of an\nadaptive re-weighting scheme to favor/penalize samples based on their level of\ncorrelation. Extensive numerical experiments have been carried out which show\nthat the proposed method is robust to the partial correlation of sources while\nstandard BSS techniques fail. The AMCA algorithm is evaluated in the field of\nastrophysics for the separation of physical components from microwave data.","url_abs":"http://arxiv.org/abs/1412.4005v1","url_pdf":"http://arxiv.org/pdf/1412.4005v1.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":"sparsity-and-adaptivity-for-the-blind","repo_url":"https://github.com/jbobin/pyGMCALab","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"blind-source-separation","task_name":"blind source separation"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1412.4005","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}