# What is expectation maximization in machine learning?

## What is expectation maximization in machine learning?

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.

### What is the EM algorithm used for?

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 used for?

The Expectation-Maximization (EM) algorithm is a way to find maximum-likelihood estimates for model parameters when your data is incomplete, has missing data points, or has unobserved (hidden) latent variables. It is an iterative way to approximate the maximum likelihood function.

What is Expectation Maximization clustering?

The EM (expectation maximization) technique is similar to the K-Means technique. Instead of assigning examples to clusters to maximize the differences in means for continuous variables, the EM clustering algorithm computes probabilities of cluster memberships based on one or more probability distributions.

## What is Expectation Maximization algorithm used for explain it with example?

Usage of EM algorithm – It can be used to fill the missing data in a sample. It can be used as the basis of unsupervised learning of clusters. It can be used for the purpose of estimating the parameters of Hidden Markov Model (HMM). It can be used for discovering the values of latent variables.

### Is expectation maximization unsupervised learning?

Expectation Maximization (EM) is a classic algorithm developed in the 60s and 70s with diverse applications. It can be used as an unsupervised clustering algorithm and extends to NLP applications like Latent Dirichlet Allocation¹, the Baum–Welch algorithm for Hidden Markov Models, and medical imaging.

What is the difference between K-means and EM?

Answer : Process of K-Means is something like assigning each observation to a cluster and process of EM(Expectation Maximization) is finding likelihood of an observation belonging to a cluster(probability). This is where both of these processes differ.

Is Em guaranteed to converge?

EM is not guaranteed to converge to a local minimum. It is only guaranteed to converge to a point with zero gradient with respect to the parameters. So it can indeed get stuck at saddle points. First of all, it is possible that EM converges to a local min, a local max, or a saddle point of the likelihood function.

## What is the difference between K means and EM?

### What is the difference between K mean and em?

What is Expectation Maximization for missing data?

Expectation maximization is applicable whenever the data are missing completely at random or missing at random-but unsuitable when the data are not missing at random. In other words, the likelihood of missing data on this variable is related to their level of depression.

Is expectation maximization supervised or unsupervised?

The Expectation Maximization (EM) algorithm is one approach to unsuper- vised, semi-supervised, or lightly supervised learning.

## What is the expectation-maximization (EM) algorithm?

The essence of Expectation-Maximization algorithm is to use the available observed data of the dataset to estimate the missing data and then using that data to update the values of the parameters. Let us understand the EM algorithm in detail. Initially, a set of initial values of the parameters are considered.

### What is the difference between expectedexpectation step and maximization step?

Expectation step (E – step): Using the observed available data of the dataset, estimate (guess) the values of the missing data. Maximization step (M – step): Complete data generated after the expectation (E) step is used in order to update the parameters. Repeat step 2 and step 3 until convergence.

What is the Gaussian mixture model (GMM)?

If they have data on customers’ purchasing history and shopping preferences, they can utilize it to predict what types of customers are more likely to purchase the new product. There are many models to solve this typical unsupervised learning problem and the Gaussian Mixture Model (GMM) is one of them.