{"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-ica-under-orthogonal-constraint","title":"Faster ICA under orthogonal constraint","arxiv_id":"1711.10873","date":"2017-11-29","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 widely used in observational sciences. In its\nclassical form, ICA relies on modeling the data as a linear mixture of\nnon-Gaussian independent sources. The problem can be seen as a likelihood\nmaximization problem. We introduce Picard-O, a preconditioned L-BFGS strategy\nover the set of orthogonal matrices, which can quickly separate both super- and\nsub-Gaussian signals. It returns the same set of sources as the widely used\nFastICA algorithm. Through numerical experiments, we show that our method is\nfaster and more robust than FastICA on real data.","url_abs":"http://arxiv.org/abs/1711.10873v1","url_pdf":"http://arxiv.org/pdf/1711.10873v1.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-ica-under-orthogonal-constraint","repo_url":"https://github.com/pierreablin/picard","is_official":1,"mentioned_in_paper":1,"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=1711.10873","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}