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arXiv · 1310.1840

Parallel coordinate descent for the Adaboost problem

Olivier Fercoq
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Abstract

We design a randomised parallel version of Adaboost based on previous studies on parallel coordinate descent. The algorithm uses the fact that the logarithm of the exponential loss is a function with coordinate-wise Lipschitz continuous gradient, in order to define the step lengths. We provide the proof of convergence for this randomised Adaboost algorithm and a theoretical parallelisation speedup factor. We finally provide numerical examples on learning problems of various sizes that show that the algorithm is competitive with concurrent approaches, especially for large scale problems.

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BibTeXRIS

Olivier Fercoq. 2013-10-07. Parallel coordinate descent for the Adaboost problem. https://doi.org/10.1109/icmla.2013.72

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