From Cutting Planes Algorithms to Compression Schemes and Active Learning
Résumé
Cutting-plane methods are well-studied localization
(and optimization) algorithms. We show that they provide a natural framework to perform machine
learning ---and not just to solve optimization problems posed by machine
learning--- in addition to their intended optimization use. In particular, they
allow one to learn sparse classifiers and provide good compression schemes.
Moreover, we show that very little effort is required to turn them into
effective active learning methods. This last property provides a generic way to
design a whole family of active learning algorithms from existing passive
methods. We present numerical simulations testifying of the relevance of
cutting-plane methods for passive and active learning tasks.
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