> ML_LITERATURE // TIBSHIRANI-1996-REGRESSION-SHRINKAGE-SELECTION-LASSO_v1.0
Regression Shrinkage and Selection via the Lasso
Robert Tibshirani · Journal of the Royal Statistical Society: Series B (Methodological) (1996)
foundational1996industry-standardthirdPartyReproduced
Principal Contribution
Introduced L1-norm penalized least squares (Lasso), simultaneously shrinking coefficients and executing sparse variable selection.
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: