5. Latent Variable & Mixture Models
Latent variable models serve as essential tools in machine learning for uncovering hidden patterns in observable data, particularly through mixture models and Gaussian Mixture Models (GMMs). The Expectation-Maximization (EM) algorithm is instrumental in estimating parameters in the presence of latent variables. While these models are powerful for tasks like clustering and density estimation, they require careful consideration of their parameters and limitations.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Latent variables help capture hidden patterns in data.
- Mixture models, particularly Gaussian Mixture Models, are significant for clustering.
- The EM algorithm facilitates parameter estimation in latent variable models.
Key Concepts
- -- Latent Variables
- Variables that are not directly observed but inferred from observable data, capturing hidden patterns.
- -- Mixture Models
- Statistical models assuming data is generated from a combination of several distributions, each representing a cluster.
- -- Gaussian Mixture Models (GMMs)
- A type of mixture model where each component is a Gaussian distribution, useful for soft clustering.
- -- ExpectationMaximization (EM) Algorithm
- A method for maximum likelihood estimation in the presence of latent variables, consisting of an E-step and M-step.
- -- AIC and BIC
- Akaike Information Criterion and Bayesian Information Criterion are methods for model selection based on likelihood.
Additional Learning Materials
Supplementary resources to enhance your learning experience.