In the Expectation-Maximization (EM) algorithm for GMM, what does the E-step compute?

Subject: Machine Learning

Topic: K-Means & Clustering in Unsupervised Learning

Options:

1. Option A: The parameters (means, covariances, mixing coefficients) using the current responsibilities.
2. Option B: The responsibilities (posterior probabilities) using the current parameter estimates.
3. Option C: The optimal number of components.
4. Option D: The initial centroids.

Correct Answer:

Option B: The responsibilities (posterior probabilities) using the current parameter estimates.

Explanation:

The E-step calculates the posterior probability (responsibility) that each data point belongs to each Gaussian component, based on current parameter estimates.