{"id":26,"date":"2004-11-28T11:05:46","date_gmt":"2004-11-28T15:05:46","guid":{"rendered":"\/?p=26"},"modified":"2005-02-09T16:15:00","modified_gmt":"2005-02-09T20:15:00","slug":"principal-components-analysis-pca","status":"publish","type":"post","link":"https:\/\/www.realclimate.org\/index.php\/archives\/2004\/11\/principal-components-analysis-pca\/","title":{"rendered":"Principal Components Analysis (“PCA”)"},"content":{"rendered":"
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A procedure by which a spatiotemporal data set is decomposed into its leading patterns in both time (see ‘Principal Component’<\/a>) and space (see ‘Empirical Orthogonal Function’<\/a>) based on an orthogonal decomposition of the data covariance matrix.<\/p>\n\n<\/div> ","protected":false},"excerpt":{"rendered":"

A procedure by which a spatiotemporal data set is decomposed into its leading patterns in both time (see ‘Principal Component’) and space (see ‘Empirical Orthogonal Function’) based on an orthogonal decomposition of the data covariance matrix.<\/p>\n","protected":false},"author":12,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[6],"tags":[],"class_list":["post-26","post","type-post","status-publish","format-standard","category-glossary","entry"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t