Expand description
Regularization Techniques for Mixture Models
This module provides various regularization techniques for mixture models, including L1 regularization for sparsity, L2 regularization for stability, elastic net for combined sparsity and stability, and group lasso for structured sparsity.
§Overview
Regularization is crucial for:
- Preventing overfitting in high-dimensional settings
- Promoting sparsity in parameter estimates
- Improving numerical stability
- Incorporating structural constraints
- Feature selection in mixture models
§Key Components
- L1 Regularization: Promotes sparsity through LASSO penalty
- L2 Regularization: Promotes stability through ridge penalty
- Elastic Net: Combines L1 and L2 penalties
- Group Lasso: Structured sparsity for grouped features
Structs§
- Elastic
NetGMM - Elastic
NetGMM Builder - Elastic
NetGMM Trained - Group
LassoGMM - Group
LassoGMM Builder - Group
LassoGMM Trained - L1RegularizedGMM
- L1 Regularized Gaussian Mixture Model
- L1RegularizedGMM
Builder - Builder for L1 Regularized GMM
- L1RegularizedGMM
Trained - Trained L1 Regularized GMM
- L2RegularizedGMM
- L2RegularizedGMM
Builder - L2RegularizedGMM
Trained
Enums§
- Regularization
Type - Type of regularization to apply