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What does Expectation Maximization do?

What does Expectation Maximization do?

The expectation-maximization algorithm is an approach for performing maximum likelihood estimation in the presence of latent variables. It does this by first estimating the values for the latent variables, then optimizing the model, then repeating these two steps until convergence.

Which technique makes use of Expectation Maximization algorithm?

The EM algorithm is used to find (local) maximum likelihood parameters of a statistical model in cases where the equations cannot be solved directly. Typically these models involve latent variables in addition to unknown parameters and known data observations.

What is expectation maximization in data mining?

In data mining, expectation-maximization (EM) is generally used as a clustering algorithm (like k-means) for knowledge discovery. In statistics, the EM algorithm iterates and optimizes the likelihood of seeing observed data while estimating the parameters of a statistical model with unobserved variables.

How many steps are there EM algorithm?

two steps
The basic two steps of the EM algorithm i.e, E-step and M-step are often pretty easy for many of the machine learning problems in terms of implementation. The solution to the M-steps often exists in the closed-form. It is always guaranteed that the value of likelihood will increase after each iteration.

Which of the following is true regarding Expectation Maximization algorithm?

2. Which of the following is untrue regarding Expectation Maximization algorithm? Explanation: The EM algorithm then consists of two steps, which are repeated consecutively. The cycle is repeated until the algorithm converges on a solution and does not change with further cycles.

What is the difference between K-means and EM?

EM and K-means are similar in the sense that they allow model refining of an iterative process to find the best congestion. However, the K-means algorithm differs in the method used for calculating the Euclidean distance while calculating the distance between each of two data items; and EM uses statistical methods.

Is expectation maximization supervised or unsupervised?

Although EM is most useful in practice for lightly supervised data, it is more easily formulated for the case of unsupervised learning.

Why is the EM algorithm useful?

The EM algorithm can be used to estimate latent variables, like ones that come from mixture distributions (you know they came from a mixture, but not which specific distribution). It works by choosing random values for the missing data points, and using those guesses to estimate a second set of data.