EM Algorithm in Machine Learning
Expectation-Maximization (EM) algorithm is another type of machine learning algorithm that falls under unsupervised learning category.
Furthermore, in this blog post, we’ll discuss its applications, and how it compares to other popular algorithms like K-means.
What is the EM Algorithm in Machine Learning?
As we already mentioned the EM algorithm, short for Expectation-Maximization, is an unsupervised learning technique.
Moreover, we can use it for finding the maximum likelihood estimates of parameters in statistical models when the data is incomplete or has missing values.
In other words, it helps to fill in the gaps when you’re working with data that has some unknowns.
How Does the EM Algorithm Work?
As its name might suggest, it works in two iterative steps: Expectation and Maximization.
In the Expectation step, we calculate the expected values of the missing data. Furthermore, we do so with the help of current estimates of the parameters.
Next, in the Maximization step, we update the parameters using the expected values the algorithm calculates in the previous step.
This process repeats until the algorithm reaches convergence, meaning the parameter estimates do not change significantly anymore.
Applications of the EM Algorithm in Machine Learning
The EM algorithm has a wide range of applications in machine learning, including:
- Gaussian Mixture Models: We use EM to estimate the parameters of Gaussian Mixture Models. These are probabilistic models that represent the distribution of data points as a combination of multiple Gaussian distributions.
- Latent Variable Models: In cases where we have hidden or unobserved variables, the EM algorithm can help estimate their values and their impact on the observed data.
- Image Segmentation: We apply the EM to separate objects in an image based on their pixel intensities or colors.
K-means vs. EM Algorithm: What’s the Difference?
While both K-means and the EM algorithm are unsupervised learning techniques, they have some notable differences.
K-means is a clustering algorithm that aims to partition the data into K distinct clusters.
Meanwhile, we use EM algorithm for estimating the parameters of statistical models when data is incomplete or has missing values.
K-means uses a simple distance metric to assign data points to the nearest cluster center, while the EM algorithm uses a probabilistic approach to estimate the likelihood of each data point belonging to different clusters.
Conclusion
In conclusion, the EM algorithm is a powerful technique in machine learning, capable of handling incomplete data and estimating parameters in complex statistical models.
By further understanding its inner workings and applications you can significantly expand your knowledge and skills in the world of machine learning.
