> ML_LITERATURE // ZOU-HASTIE-2005-REGULARIZATION-VARIABLE-SELECTION-ELASTIC-NET_v1.0
Regularization and variable selection via the elastic net
Hui Zou, Trevor Hastie · Journal of the Royal Statistical Society: Series B (Statistical Methodology) (2005)
foundational2005industry-standardthirdPartyReproduced
Principal Contribution
Proposed the Elastic Net penalty combining L1 sparsity with L2 grouped selection, overcoming Lasso limitations when p >> n.
Operational Relevance
Serves as canonical technical reference for implementing task-regression, task-feature-extraction in production systems.
Assumptions
- Underlying data distribution satisfies empirical consistency and regularity assumptions across training domains
Limitations
- Scaling characteristics and accuracy depend on hardware architecture, parameter scale, and dataset quality
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: