We consider L2Boosting, a special case of Friedman's generic boosting algorithm applied to linear regression under L2-loss. We study L2Boosting for an arbitrary regularization parameter and derive an exact closed form expression for the number of steps taken along a fixed coordinate direction. This relationship is used to describe L2Boosting's solution path, to describe new tools for studying its path, and to characterize some of the algorithm's unique properties, including active set cycling, a property where the algorithm spends lengthy periods of time cycling between the same coordinates when the regularization parameter is arbitrarily small. Our fixed descent analysis also reveals a repressible condition that limits the effectiveness of L2Boosting in correlated problems by preventing desirable variables from entering the solution path. As a simple remedy, a data augmentation method similar to that used for the elastic net is used to introduce L2-penalization and is shown, in combination with decorrelation, to reverse the repressible condition and circumvents L2Boosting's deficiencies in correlated problems. In itself, this presents a new explanation for why the elastic net is successful in correlated problems and why methods like LAR and lasso can perform poorly in such settings.
- Critical direction
- Solution path
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty