What is Gaussian Mixture Model (GMM)?
Gaussian Mixture Model (GMM) is a probabilistic model used in unsupervised learning. It assumes that the data points are generated from a mixture of several Gaussian distributions, each representing a different cluster or subgroup within the data. The key idea is to capture the underlying distribution of the data by estimating the parameters (mean and covariance) of these multiple Gaussian components.
Core Components
- Gaussian Distributions: Each cluster in a GMM is represented by a Gaussian distribution characterized by a specific mean and covariance.
- Mixture Weights: Each Gaussian component has an associated weight that signifies its contribution to the overall model.
- Expectation-Maximization (EM) Algorithm: GMM commonly uses the EM algorithm to iteratively estimate the parameters of the model until convergence.
Applications
GMMs are widely used for tasks such as clustering, anomaly detection, and density estimation. They are particularly effective in scenarios where the data exhibits multi-modality, as multiple Gaussian distributions can effectively represent complex data distributions.
Conclusion
In summary, Gaussian Mixture Models provide a flexible approach to capturing the structure within data in unsupervised learning scenarios, allowing for the identification of distinct clusters and patterns.