What does "Expectation-maximization Algorithm" mean?
Table of Contents
The Expectation-Maximization (EM) algorithm is a method used to find the best estimates for complex models when some data is missing or unclear. It helps make sense of data by breaking down the process into two main steps: the expectation step and the maximization step.
How It Works
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Expectation Step: In this step, the algorithm makes an initial guess about the missing data or the parts of the data we don't fully understand. It uses the current estimates to fill in these gaps.
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Maximization Step: Once the missing data is estimated, the algorithm updates the model parameters to make the best fit with the entire dataset, including the filled-in gaps.
These steps are repeated multiple times, with the algorithm gradually improving the estimates until they stabilize, or stop changing significantly.
Applications
The EM algorithm is used in various fields such as statistics, machine learning, and data analysis. It's particularly helpful in situations where the data is high-dimensional or when dealing with complex patterns like clusters of data. It allows for better predictions and insights by refining model parameters, making it easier to analyze and understand the data we have.