1.0Glosarixhttps://glosarix.com/en/Team Glosarixhttps://glosarix.com/en/author/adm_glosarix/rich600338<blockquote class="wp-embedded-content" data-secret="JqDq76glCL"><a href="https://glosarix.com/en/glossary/principal-component-analysis-en/">Principal component analysis</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://glosarix.com/en/glossary/principal-component-analysis-en/embed/#?secret=JqDq76glCL" width="600" height="338" title="“Principal component analysis” — Glosarix" data-secret="JqDq76glCL" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document); //# sourceURL=https://glosarix.com/wp-includes/js/wp-embed.min.js </script> Description: Principal Component Analysis (PCA) is a statistical procedure that transforms a set of observations of possibly correlated variables into a set of values of uncorrelated variables, called principal components. This method aims to reduce the dimensionality of the data, making it easier to analyze and visualize while retaining as much variability as possible. PCA […]