{"id":1349,"date":"2020-05-14T17:59:21","date_gmt":"2020-05-14T12:29:21","guid":{"rendered":"https:\/\/www.codeavail.com\/blog\/?p=1349"},"modified":"2024-09-13T18:11:03","modified_gmt":"2024-09-13T12:41:03","slug":"what-is-em-algorithm-in-machine-learning-and-how-it-works","status":"publish","type":"post","link":"https:\/\/www.codeavail.com\/blog\/what-is-em-algorithm-in-machine-learning-and-how-it-works\/","title":{"rendered":"What is EM Algorithm in Machine Learning and how it works?"},"content":{"rendered":"\n<p>Here CodeAvail experts will explain to you what is EM Algorithm in machine learning and how it works.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"em-algorithm-and-machine-learning\"><\/span>EM Algorithm and Machine Learning<span class=\"ez-toc-section-end\"><\/span><\/h2><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_69_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69e42b5106a81\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69e42b5106a81\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.codeavail.com\/blog\/what-is-em-algorithm-in-machine-learning-and-how-it-works\/#em-algorithm-and-machine-learning\" title=\"EM Algorithm and Machine Learning\">EM Algorithm and Machine Learning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.codeavail.com\/blog\/what-is-em-algorithm-in-machine-learning-and-how-it-works\/#what-is-em-algorithm-in-machine-learning\" title=\"What is EM Algorithm In Machine Learning?\">What is EM Algorithm In Machine Learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.codeavail.com\/blog\/what-is-em-algorithm-in-machine-learning-and-how-it-works\/#how-does-em-algorithm-work\" title=\"How Does EM Algorithm Work?\">How Does EM Algorithm Work?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.codeavail.com\/blog\/what-is-em-algorithm-in-machine-learning-and-how-it-works\/#flow-chart-of-expectation-maximization-algorithm\" title=\"Flow chart of Expectation-Maximization Algorithm\">Flow chart of Expectation-Maximization Algorithm<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.codeavail.com\/blog\/what-is-em-algorithm-in-machine-learning-and-how-it-works\/#gaussian-mixture-model\" title=\"Gaussian Mixture Model\">Gaussian Mixture Model<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.codeavail.com\/blog\/what-is-em-algorithm-in-machine-learning-and-how-it-works\/#applications-of-em-algorithm\" title=\"Applications Of EM Algorithm\">Applications Of EM Algorithm<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.codeavail.com\/blog\/what-is-em-algorithm-in-machine-learning-and-how-it-works\/#uses-of-expectation-maximization-algorithm-%e2%80%93\" title=\"Uses of Expectation-Maximization algorithm \u2013\">Uses of Expectation-Maximization algorithm \u2013<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.codeavail.com\/blog\/what-is-em-algorithm-in-machine-learning-and-how-it-works\/#advantages-of-expectation-maximization-algorithm-%e2%80%93\" title=\"Advantages of Expectation-Maximization algorithm \u2013\">Advantages of Expectation-Maximization algorithm \u2013<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.codeavail.com\/blog\/what-is-em-algorithm-in-machine-learning-and-how-it-works\/#disadvantages-of-expectation-maximization-algorithm-%e2%80%93\" title=\"Disadvantages of Expectation-Maximization algorithm \u2013\">Disadvantages of Expectation-Maximization algorithm \u2013<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.codeavail.com\/blog\/what-is-em-algorithm-in-machine-learning-and-how-it-works\/#conclusion\" title=\"Conclusion\">Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n\n\n\n\n<p>In reality utilization of Machine Learning, it is basic that there are numerous applicable highlights accessible for learning. However, just a little subset of them is noticeable. In this way, for the factors which are now and sometimes noticeable and sometimes not. At that point, we can utilize the occasions. When that variable is accessible is seen to learn and afterward anticipate. Its purpose in the occurrences when it isn&#8217;t observable.<\/p>\n\n\n\n<p>Then again, the Expectation-Maximization algorithm(EM) can be utilized for the dormant (factors that are not easily recognizable. And are really gathered from the estimations of the other observed factors). In order to anticipate their qualities with the condition that the general type of probability distribution overseeing those inactive factors is known to us. <\/p>\n\n\n\n<p>Expectation-Maximization algorithm is really at the base of numerous unaided clustering algorithms in the Machine learning field. It was clarified, proposed, and given its name in a paper distributed in 1977 by Arthur Dempster, Nan Laird, and Donald Rubin. It is utilized to determine the nearby greatest probability parameters of a statistical model in situations. Where dormant factors are included and the information is absent or deficient.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter size-full\"><img decoding=\"async\" width=\"1071\" height=\"2451\" src=\"https:\/\/www.codeavail.com\/blog\/wp-content\/uploads\/2020\/05\/What-is-EM-Algorithm-in-Machine-Learning-and-how-it-works-1.png\" alt=\"What is EM Algorithm in Machine Learning and how it works\" class=\"wp-image-1373\" srcset=\"https:\/\/www.codeavail.com\/blog\/wp-content\/uploads\/2020\/05\/What-is-EM-Algorithm-in-Machine-Learning-and-how-it-works-1.png 1071w, https:\/\/www.codeavail.com\/blog\/wp-content\/uploads\/2020\/05\/What-is-EM-Algorithm-in-Machine-Learning-and-how-it-works-1-131x300.png 131w, https:\/\/www.codeavail.com\/blog\/wp-content\/uploads\/2020\/05\/What-is-EM-Algorithm-in-Machine-Learning-and-how-it-works-1-447x1024.png 447w, https:\/\/www.codeavail.com\/blog\/wp-content\/uploads\/2020\/05\/What-is-EM-Algorithm-in-Machine-Learning-and-how-it-works-1-768x1758.png 768w, https:\/\/www.codeavail.com\/blog\/wp-content\/uploads\/2020\/05\/What-is-EM-Algorithm-in-Machine-Learning-and-how-it-works-1-671x1536.png 671w, https:\/\/www.codeavail.com\/blog\/wp-content\/uploads\/2020\/05\/What-is-EM-Algorithm-in-Machine-Learning-and-how-it-works-1-895x2048.png 895w\" sizes=\"(max-width: 1071px) 100vw, 1071px\" \/><figcaption>What is EM Algorithm in Machine Learning and how it works<\/figcaption><\/figure><\/div>\n\n\n\n<p>In this article, we have given all the essential information regarding <strong>what is EM algorithm in machine learning<\/strong>.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"what-is-em-algorithm-in-machine-learning\"><\/span>What is EM Algorithm In Machine Learning?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Expectation-Maximization algorithm was introduced in 1997 by Arthur Dempster, Nan Laird, and Donald Rubin. It is utilized to find the local maximum statistical model probability parameters. In case the possible variables are existing or the data is missing or inadequate.<\/p>\n\n\n\n<p>The Expectation-Maximization Algorithm supports the following steps to determine the appropriate model parameters in the presence of latent variables.<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li>Analyze a collection of exciting parameters in inadequate data.<\/li><li><strong>Expectation Step<\/strong> \u2013 This step is utilized to determine the values of the lost values in the data. It includes the recognized data to guess the values in the lost data.<\/li><li><strong>Maximization<\/strong> <strong>Step <\/strong>\u2013 This step makes whole data after the Expectation step updates the lost values in the data.<\/li><li>Perform the step <strong>Expectation Step <\/strong>and <strong>Maximization<\/strong> <strong>Step <\/strong>until the convergence is met.<\/li><\/ol>\n\n\n\n<p><strong>Convergence<\/strong>\u2013 The idea of union in likelihood depends on the intuition. Suppose we have two irregular factors if the likelihood of their distinction is little, it is supposed to be converged. For this situation, convergence implies if the qualities coordinate one another.&nbsp;<\/p>\n\n\n\n<p>Since we understand what is the Expectation-Maximization algorithm in Machine Learning. Let us investigate how it really functions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"how-does-em-algorithm-work\"><\/span>How Does EM Algorithm Work?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The fundamental concept behind the Expectation-Maximization algorithm is to utilize the found data to determine the lost data then after updating those parameter values. Above we have discussed what is EM algorithm in Machine learning now having the flowchart in mind. Let us know how the Expectation-Maximization algorithm works.<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li>In the opening stage, a collection of primary parameters is examined. A set of incomplete and unobserved data is provided to the system with an assumption that the detected data is coming from a particular form.<\/li><li>After that next step is the <strong>Expectation Step<\/strong> or <strong>E-STEP<\/strong>. In this stage, you use the detected data to determine lost or inadequate data. It is utilized to update the variables.<\/li><li>Then the next step is the <strong>Maximization step<\/strong> or <strong>M-STEP<\/strong> is utilized to create the data produced in the E-STEP. This step performs the hypothesis updation.<\/li><li>In the final step, it is verified whether the values are converging or not. If the values match, then there is no need to do anything, else we will proceed with the <strong>Expectation Step<\/strong> and <strong>Maximization step <\/strong>until the convergence is met.<\/li><\/ol>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"flow-chart-of-expectation-maximization-algorithm\"><\/span>Flow chart of Expectation-Maximization Algorithm<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh6.googleusercontent.com\/vKFjby3aHcLlDDpDeQcA_hsNI-XPDiAqf4OA49UWqnbyRxbtoPZNxpNZsuwBVQT3nA-r05lnMFNFRoc0VNBQ0-5gLlqLcglJ6bTyj1wtHmdBRB95AAJheLMlZTNuk3tjuU3lp0r4\" alt=\"Flow chart of Expectation-Maximization Algorithm\"\/><figcaption>Flow chart of Expectation-Maximization Algorithm<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"gaussian-mixture-model\"><\/span>Gaussian Mixture Model<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The <strong>Gaussian Mixture Model<\/strong> is a hybrid model that utilizes a mixture of likelihood numbers and also needs the calculation of mean and standard variation parameters.<\/p>\n\n\n\n<p>Even though there are many ways to determine the Gaussian Mixture Model parameters, the most popular method is the Maximum Probability estimation.<\/p>\n\n\n\n<p>Let us suppose a case, where the information points are created by two distinct procedures, and each procedure has a Gaussian likelihood distribution. Be that as it may, it is confusing, which dissemination a given information point belongs to since the information is connected and distribution is comparable. <\/p>\n\n\n\n<p>What&#8217;s more, the procedures utilized for creating the information points represent the inactive factors and impact the information. The EM algorithm appears the best way to deal with measure the parameters of the distributions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"applications-of-em-algorithm\"><\/span>Applications Of EM Algorithm<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li>Expectation-Maximization Algorithm is usually utilized in information clustering in ML and computer vision.<\/li><li>Expectation-Maximization also utilized in natural language processing.<\/li><li>The Expectation-Maximization algorithm is utilized for estimation of the parameter in mixed models and quantitative genetics<\/li><li>It is utilized in psychometrics for determining item parameters and potential capabilities of item response theory models.<\/li><li>other applications incorporate medical image restoration, structural engineering, etc.<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"uses-of-expectation-maximization-algorithm-%e2%80%93\"><\/span>Uses of Expectation-Maximization algorithm \u2013<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li>EM can be utilized to fulfill the lost data in a sample.<\/li><li>Expectation-Maximization can be utilized as the basis of unsupervised knowledge of clusters.<\/li><li>It can be utilized to determine the parameters of the Hidden Markov Model (HMM).<\/li><li>It can be utilized for determining the values of the latent variables.<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"advantages-of-expectation-maximization-algorithm-%e2%80%93\"><\/span>Advantages of Expectation-Maximization algorithm \u2013<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li>Expectation-Maximization always guarantees that probability will grow with each iteration.<\/li><li>The <strong>Expectation<\/strong>-step and <strong>Maximization<\/strong>-step are usually pretty simple for several problems in times of implementation.<\/li><li>Answers to the <strong>Maximization<\/strong>-steps usually exist in the concluded form.<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"disadvantages-of-expectation-maximization-algorithm-%e2%80%93\"><\/span>Disadvantages of Expectation-Maximization algorithm \u2013<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li>Expectation-Maximization has late convergence.<\/li><li>EM performs convergence to the limited optima only.<\/li><li>It needs both the possibilities, backward and forward (numerical optimization needs only forward possibility).<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>To this end, we have included all the relevant information regarding your problem,\u201d <strong>what is EM algorithm in machine learning\u201d. <\/strong>We hope that you find this article helpful.<\/p>\n\n\n\n<p>However, if you come across any problem regarding Expectation-Maximization in machine learning you can contact us anytime and from anywhere in the world. As we are always available for your service.<\/p>\n\n\n\n<p>If you want to get <a href=\"https:\/\/www.codeavail.com\/Machine-Learning-Assignment-Help\">machine learning assignment help<\/a> and <a href=\"https:\/\/www.codeavail.com\/Algorithm-Assignment-Help\">algorithm assignment help<\/a>, you can ask our experts to <a href=\"https:\/\/www.codeavail.com\/submit-work\" class=\"rank-math-link\">submit <\/a>their queries.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Here CodeAvail experts will explain to you what is EM Algorithm in machine learning and how it works. EM Algorithm and Machine Learning In reality utilization of Machine Learning, it is basic that there are numerous applicable highlights accessible for learning. However, just a little subset of them is noticeable. In this way, for the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1351,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"rank_math_lock_modified_date":false,"site-sidebar-layout":"default","site-content-layout":"default","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"default","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[47,307],"tags":[447,446],"class_list":["post-1349","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-education","category-computer-science","tag-em-algorithm-in-machine-learning","tag-what-is-em-algorithm-in-machine-learning"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/www.codeavail.com\/blog\/wp-json\/wp\/v2\/posts\/1349","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.codeavail.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.codeavail.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.codeavail.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.codeavail.com\/blog\/wp-json\/wp\/v2\/comments?post=1349"}],"version-history":[{"count":2,"href":"https:\/\/www.codeavail.com\/blog\/wp-json\/wp\/v2\/posts\/1349\/revisions"}],"predecessor-version":[{"id":34801,"href":"https:\/\/www.codeavail.com\/blog\/wp-json\/wp\/v2\/posts\/1349\/revisions\/34801"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.codeavail.com\/blog\/wp-json\/wp\/v2\/media\/1351"}],"wp:attachment":[{"href":"https:\/\/www.codeavail.com\/blog\/wp-json\/wp\/v2\/media?parent=1349"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.codeavail.com\/blog\/wp-json\/wp\/v2\/categories?post=1349"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.codeavail.com\/blog\/wp-json\/wp\/v2\/tags?post=1349"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}