{"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/faster-independent-component-analysis-by","title":"Faster independent component analysis by preconditioning with Hessian approximations","arxiv_id":"1706.08171","date":"2017-06-25","proceeding":null,"authors":["Pierre Ablin","Jean-François Cardoso","Alexandre Gramfort"],"abstract":"Independent Component Analysis (ICA) is a technique for unsupervised\nexploration of multi-channel data that is widely used in observational\nsciences. In its classic form, ICA relies on modeling the data as linear\nmixtures of non-Gaussian independent sources. The maximization of the\ncorresponding likelihood is a challenging problem if it has to be completed\nquickly and accurately on large sets of real data. We introduce the\nPreconditioned ICA for Real Data (Picard) algorithm, which is a relative L-BFGS\nalgorithm preconditioned with sparse Hessian approximations. Extensive\nnumerical comparisons to several algorithms of the same class demonstrate the\nsuperior performance of the proposed technique, especially on real data, for\nwhich the ICA model does not necessarily hold.","url_abs":"http://arxiv.org/abs/1706.08171v3","url_pdf":"http://arxiv.org/pdf/1706.08171v3.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":"faster-independent-component-analysis-by","repo_url":"https://github.com/pierreablin/faster-ica","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"faster-independent-component-analysis-by","repo_url":"https://github.com/pierreablin/picard","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[{"method_slug":"ica","method_name":"ICA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.08171","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}