{"id":5760,"date":"2023-09-03T16:21:16","date_gmt":"2023-09-03T14:21:16","guid":{"rendered":"https:\/\/francestat.com\/?page_id=5760"},"modified":"2024-12-02T11:23:12","modified_gmt":"2024-12-02T10:23:12","slug":"uniwin-ica","status":"publish","type":"page","link":"https:\/\/francestat.com\/index.php\/uniwin-ica\/","title":{"rendered":"Uniwin – ICA"},"content":{"rendered":"

[vc_row][vc_column]

<\/div>

UNIWIN - Analyse en Composantes Ind\u00e9pendantes<\/h2><\/div><\/div>[\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]L’Analyse en composantes ind\u00e9pendantes (Independent Component Analysis – ICA) est une m\u00e9thode d’analyse des donn\u00e9es qui rel\u00e8ve des statistiques, des r\u00e9seaux de neurones et du traitement du signal. Elle est notoirement et historiquement connue en tant que m\u00e9thode de s\u00e9paration aveugle de signaux m\u00e9lang\u00e9s dans des proportions inconnues et est aujourd’hui appliqu\u00e9e \u00e0 de nombreux probl\u00e8mes, notamment en chimiom\u00e9trie pour l’analyse de spectres (source Wikip\u00e9dia).<\/p>\n

Cette proc\u00e9dure du logiciel utilise les packages R \u2018ica\u2019 et \u2018JADE\u2019.[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_single_image image=\u00a0\u00bb6384″ img_size=\u00a0\u00bblarge\u00a0\u00bb alignment=\u00a0\u00bbcenter\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]<\/p>\n

Tableaux<\/u><\/span><\/strong><\/p>\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Matrice de rotation initiale<\/p>\n<\/td>\n<\/tr>\n

\n

Matrice de m\u00e9lange estim\u00e9e<\/p>\n<\/td>\n<\/tr>\n

\n

Matrice de d\u00e9m\u00e9lange estim\u00e9e<\/p>\n<\/td>\n<\/tr>\n

\n

Composantes principales<\/p>\n<\/td>\n<\/tr>\n

\n

Matrice de blanchiment estim\u00e9e<\/p>\n<\/td>\n<\/tr>\n

\n

Matrice de rotation estim\u00e9e<\/p>\n<\/td>\n<\/tr>\n

\n

Composantes ind\u00e9pendantes<\/p>\n<\/td>\n<\/tr>\n

\n

Variances prises en compte<\/p>\n<\/td>\n<\/tr>\n

\n

R\u00e9sidus<\/p>\n<\/td>\n<\/tr>\n

\n

Coefficients de Durbin-Watson<\/p>\n<\/td>\n<\/tr>\n

\n

Coefficient de Kayser-Meyer-Olkin (KMO)<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Graphiques<\/u><\/span><\/strong><\/p>\n\n\n<\/colgroup>\n\n\n\n\n\n\n\n
\n

Graphique des donn\u00e9es initiales<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique d’une composante principale<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique d’une composante ind\u00e9pendante<\/p>\n<\/td>\n<\/tr>\n

\n

Histogramme des donn\u00e9es initiales<\/p>\n<\/td>\n<\/tr>\n

\n

Histogramme des donn\u00e9es estim\u00e9es<\/p>\n<\/td>\n<\/tr>\n

\n

Graphique des coefficients de Durbin-Watson<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space height=\u00a0\u00bb5px\u00a0\u00bb][\/vc_column][\/vc_row][vc_row][vc_column][vc_btn title=\u00a0\u00bbConsulter la documentation compl\u00e8te\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb link=\u00a0\u00bburl:https%3A%2F%2Ffrancestat.com%2Ftelecharg%2FUniwin%2Fpdf%2FAnalyse%2520en%2520composantes%2520ind%25e9pendantes.pdf|title:UNIWIN%20-%20ICA\u00a0\u00bb][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]L’Analyse en composantes ind\u00e9pendantes (Independent Component Analysis – ICA) est une m\u00e9thode d’analyse des donn\u00e9es qui rel\u00e8ve des statistiques, des r\u00e9seaux de neurones et du traitement du signal. Elle est notoirement et historiquement connue en tant que m\u00e9thode de s\u00e9paration aveugle de signaux m\u00e9lang\u00e9s dans des proportions inconnues et est aujourd’hui appliqu\u00e9e \u00e0 de nombreux…<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-5760","page","type-page","status-publish","hentry","description-off"],"yoast_head":"\nUniwin - ICA - FRANCESTAT<\/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:\/\/francestat.com\/index.php\/uniwin-ica\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Uniwin - ICA - FRANCESTAT\" \/>\n<meta property=\"og:description\" content=\"[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]L’Analyse en composantes ind\u00e9pendantes (Independent Component Analysis – ICA) est une m\u00e9thode d’analyse des donn\u00e9es qui rel\u00e8ve des statistiques, des r\u00e9seaux de neurones et du traitement du signal. Elle est notoirement et historiquement connue en tant que m\u00e9thode de s\u00e9paration aveugle de signaux m\u00e9lang\u00e9s dans des proportions inconnues et est aujourd’hui appliqu\u00e9e \u00e0 de nombreux…\" \/>\n<meta property=\"og:url\" content=\"https:\/\/francestat.com\/index.php\/uniwin-ica\/\" \/>\n<meta property=\"og:site_name\" content=\"FRANCESTAT\" \/>\n<meta property=\"article:modified_time\" content=\"2024-12-02T10:23:12+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-ica\\\/\",\"url\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-ica\\\/\",\"name\":\"Uniwin - ICA - FRANCESTAT\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#website\"},\"datePublished\":\"2023-09-03T14:21:16+00:00\",\"dateModified\":\"2024-12-02T10:23:12+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-ica\\\/#breadcrumb\"},\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-ica\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/francestat.com\\\/index.php\\\/uniwin-ica\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Accueil\",\"item\":\"https:\\\/\\\/francestat.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Uniwin – ICA\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#website\",\"url\":\"https:\\\/\\\/francestat.com\\\/\",\"name\":\"FRANCESTAT\",\"description\":\"Logiciels, formations et services statistiques\",\"publisher\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/francestat.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#organization\",\"name\":\"FRANCESTAT\",\"url\":\"https:\\\/\\\/francestat.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\\\/\\\/francestat.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/francestat.com\\\/wp-content\\\/uploads\\\/2018\\\/05\\\/logo_horizontal_small_francestat.jpg\",\"contentUrl\":\"https:\\\/\\\/francestat.com\\\/wp-content\\\/uploads\\\/2018\\\/05\\\/logo_horizontal_small_francestat.jpg\",\"width\":155,\"height\":51,\"caption\":\"FRANCESTAT\"},\"image\":{\"@id\":\"https:\\\/\\\/francestat.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Uniwin - ICA - FRANCESTAT","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/francestat.com\/index.php\/uniwin-ica\/","og_locale":"fr_FR","og_type":"article","og_title":"Uniwin - ICA - FRANCESTAT","og_description":"[vc_row][vc_column][\/vc_column][\/vc_row][vc_row][vc_column][vc_empty_space][\/vc_column][\/vc_row][vc_row][vc_column][vc_column_text]L’Analyse en composantes ind\u00e9pendantes (Independent Component Analysis – ICA) est une m\u00e9thode d’analyse des donn\u00e9es qui rel\u00e8ve des statistiques, des r\u00e9seaux de neurones et du traitement du signal. 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