{"id":394,"date":"2016-07-14T16:35:43","date_gmt":"2016-07-14T19:35:43","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=394"},"modified":"2016-07-14T16:35:43","modified_gmt":"2016-07-14T19:35:43","slug":"vol5-no2-art3","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol5-no2\/vol5-no2-art3\/","title":{"rendered":"Nonlinear Independent Component Analysis: Theoretical Review And Applications"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> Nonlinear Independent Component Analysis: Theoretical Review And Applications<\/p>\n<p><strong>Autores:<\/strong> Simas Filho, E. F.; Seixas, J. M.<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> This paper reviews the Nonlinear Independent Components Analysis and its applications to blind source separation. An overview of the main statistical principles that guide the search for the independent components is formulated. The uniqueness of solution and some algorithms for estimating the nonlinear independent components are discussed. Experimental results using a synthetic database are used for performance comparison. A practical application in experimental high-energy physics is also presented.<\/p>\n<p><strong>Palavras-chave:<\/strong> Nonlinear ICA; Neural Networks; Blind Source Separation; Nonlinear Mixtures; Signal Detection<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 22<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol5-no2-art3\">10.21528\/lmln-vol5-no2-art3<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol5-no2-art3.pdf\" rel=\"\">vol5-no2-art3.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol5-no2-art3.bib\" rel=\"\">vol5-no2-art3.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: Nonlinear Independent Component Analysis: Theoretical Review And Applications Autores: Simas Filho, E. F.; Seixas, J. M. Resumo: This paper reviews the Nonlinear Independent Components Analysis and its applications to blind source separation. An overview of the main statistical principles <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol5-no2\/vol5-no2-art3\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":388,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-394","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Nonlinear Independent Component Analysis: Theoretical Review And Applications - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol5-no2\/vol5-no2-art3\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Nonlinear Independent Component Analysis: Theoretical Review And Applications - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: Nonlinear Independent Component Analysis: Theoretical Review And Applications Autores: Simas Filho, E. 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