Parallel Boosting with Momentum
Venue
ECML PKDD 2013, Part III, LNAI 8190, Springer, Heidelberg, pp. 17-32 (to appear)
Publication Year
2013
Authors
Indraneel Mukherjee, Kevin Canini, Rafael Frongillo, Yoram Singer
BibTeX
Abstract
We describe a new, simplified, and general analysis of a fusion of Nesterov’s
accelerated gradient with parallel coordinate descent. The resulting algorithm,
which we call BOOM, for boosting with momentum, enjoys the merits of both
techniques. Namely, BOOM retains the momentum and convergence properties of the
accelerated gradient method while taking into account the curvature of the
objective function. We describe a distributed implementation of BOOM which is
suitable for massive high dimensional datasets. We show experimentally that BOOM is
especially effective in large scale learning problems with rare yet informative
features.
