Expectation Maximization
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Expectation Maximization
Expectation maximization (EM) is an algorithm that finds the best estimates for model parameters when a dataset is missing information or has hidden latent variables. While this technique can be used to determine the maximum likelihood function, or the “best fit” model for a set of data, EM takes things a step further and works on incomplete data sets. This is achieved by inserting random values for the missing data points, and then estimating a second set of data. The new dataset is used to refine the guesses added to the first, with the process repeating until the algorithm’s termination criterion are met.
Contributeurs: Claire Gorjux, wiki
