Parallel boosting with momentum

Indraneel Mukherjee, Kevin Canini, Rafael Frongillo, Yoram Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Proceedings
Pages17-32
Number of pages16
EditionPART 3
DOIs
StatePublished - Oct 31 2013
Externally publishedYes
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013 - Prague, Czech Republic
Duration: Sep 23 2013Sep 27 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8190 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2013
CountryCzech Republic
CityPrague
Period9/23/139/27/13

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • accelerated gradient
  • boosting
  • coordinate descent

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  • Cite this

    Mukherjee, I., Canini, K., Frongillo, R., & Singer, Y. (2013). Parallel boosting with momentum. In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Proceedings (PART 3 ed., pp. 17-32). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8190 LNAI, No. PART 3). https://doi.org/10.1007/978-3-642-40994-3_2