The primary goal of the EM algorithm is to use the available observed data of the dataset to estimate the missing data of the latent variables and then use that data to update the values of the parameters in the M-step. Repeat E-step and M-step until the convergence of the values occurs.Maximization step (M - step): This step involves the use of estimated data in the E-step and updating the parameters.Expectation step (E - step): It involves the estimation (guess) of all missing values in the dataset so that after completing this step, there should not be any missing value.The second mode is known as the maximization-step or M-step. Further, the other mode is used to optimize the parameters of the models so that it can explain the data more clearly. Hence it is referred to as the Expectation/estimation step (E-step). In the first mode, we estimate the missing or latent variables. Being an iterative approach, it consists of two modes. The EM algorithm is the combination of various unsupervised ML algorithms, such as the k-means clustering algorithm. It is used to predict values of parameters in instances where data is missing or unobservable for learning, and this is done until convergence of the values occurs.It is known as the latent variable model to determine MLE and MAP parameters for latent variables.These unobservable variables are known as latent variables. It is also referred to as the latent variable model.Ī latent variable model consists of both observable and unobservable variables where observable can be predicted while unobserved are inferred from the observed variable. Further, it is a technique to find maximum likelihood estimation when the latent variables are present. The Expectation-Maximization (EM) algorithm is defined as the combination of various unsupervised machine learning algorithms, which is used to determine the local maximum likelihood estimates (MLE) or maximum a posteriori estimates (MAP) for unobservable variables in statistical models. In this topic, we will discuss a basic introduction to the EM algorithm, a flow chart of the EM algorithm, its applications, advantages, and disadvantages of EM algorithm, etc. On the other hand, the variables which are latent or directly not observable, for such variables Expectation-Maximization (EM) algorithm plays a vital role to predict the value with the condition that the general form of probability distribution governing those latent variables is known to us. If the variables are observable, then it can predict the value using instances. In most real-life applications of machine learning, it is found that several relevant learning features are available, but very few of them are observable, and the rest are unobservable. It has various real-world applications in statistics, including obtaining the mode of the posterior marginal distribution of parameters in machine learning and data mining applications. However, it is also applicable to unobserved data or sometimes called latent. The EM (Expectation-Maximization) algorithm is one of the most commonly used terms in machine learning to obtain maximum likelihood estimates of variables that are sometimes observable and sometimes not. The EM algorithm is considered a latent variable model to find the local maximum likelihood parameters of a statistical model, proposed by Arthur Dempster, Nan Laird, and Donald Rubin in 1977. Next → ← prev EM Algorithm in Machine Learning
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