Branchennorm
Branchennorm is a term used in machine learning and statistics to denote a structured sparsity-inducing norm defined for vectors whose components are organized in a branching, tree-like structure. It is designed to encourage sparsity that respects the hierarchy, yielding models where either an entire branch of features is selected or rejected, improving interpretability and reducing overfitting in hierarchical data.
A common formulation considers a rooted tree T with leaf indices L. Let x ∈ R^{|L|} be the
Properties and relationships: the branchennorm is a convex regularizer under a laminar group structure, and it
Applications: branchennorm has been used to regularize models with inherently hierarchical features, including decision-tree ensembles, parse-tree
See also: group lasso, tree-structured sparsity, hierarchical lasso.