Gaussian Mixture Models

What are Gaussian Mixture Models?

Gaussian Mixture Models (GMMs) are a probabilistic model used for clustering, density estimation, and data generation. GMMs represent a mixture of multiple Gaussian distributions, each with its own mean and covariance matrix. The goal of GMMs is to find the optimal parameters of these Gaussian distributions to best fit the given data.

How do Gaussian Mixture Models work?

GMMs work by estimating the parameters of the Gaussian distributions using an iterative algorithm called Expectation-Maximization (EM). The EM algorithm alternates between two steps: the expectation step, where the posterior probabilities of the data points belonging to each Gaussian component are computed, and the maximization step, where the parameters of the Gaussian components are updated based on the computed probabilities. The EM algorithm converges to a local maximum of the likelihood function, providing the final Gaussian mixture model.

Example of Gaussian Mixture Model clustering with Python and scikit-learn

import numpy as np
import matplotlib.pyplot as plt
from sklearn.mixture import GaussianMixture

# Generate sample data
data = np.vstack([np.random.normal(0, 1, size=(100, 2)),
                  np.random.normal(4, 1, size=(100, 2))])

# Fit a Gaussian Mixture Model with two components
gmm = GaussianMixture(n_components=2)

# Predict the cluster labels
labels = gmm.predict(data)

# Visualize the clustering results
plt.scatter(data[:, 0], data[:, 1], c=labels, cmap='viridis')
plt.title('GMM Clustering')

In this example, we generate two clusters of data points with Gaussian distributions and use a GMM with two components to fit the data. The resulting plot shows the clustering results based on the fitted GMM.

Resources for learning Gaussian Mixture Models