Factorized AdaBoost.MH Achieves the Same Convergence Rate as AdaBoost.MH
{AdaBoost.MH} reduces multi-class classification to a collection of binary subproblems and enjoys the classical boosting-type convergence guarantee under a weak learning condition. A more structured variant, Factorized {AdaBoost.MH}, uses base classifiers of the form $\mathbf{h}(x)=α\mathbf{v} \bm{\varphi}(x)$, where a single binary classifier $\bm{\varphi}$ is shared across all classes and the label dependence is carried by a vote vector $\mathbf{v} \in\{\pm1\}^K$. This factorization is algorithmically attractive and achieves better performance in practice, but its convergence depends on whether one can always choose a vote vector with sufficiently large induced binary weight mass. Previ...
| Authors | Xin Zou, Jingyuan Xu |
|---|---|
| Primary category | cs.LG |
| Published | 2 August 2026 |
| Last updated at source | 8 August 2026 |
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