Parallel Distributed Kernel Estimation
Computational Statistics and Data Analysis, 40 (2):
293-302 (2002)This might be good to look at when looking at kernel methods..Abstract
Non-parametric kernel methods are becoming more commonplace for data
analysis, modeling, and inference. Unfortunately, these methods
are known to be computationally burdensome. The burden increases
as the amount of available data rises and can quickly overwhelm
the computational resources present in modern desktop workstations.
Approximation-based approaches exist which can dramatically reduce
execution time, however, these approaches remain just that??approximations,
however good they may be. Along with the approximate nature of such
approaches, they do not admit multivariate kernel estimation with
general bandwidths (fixed, variable, and adaptive). In this paper,
I consider a parallel implementation of a number of popular kernel
methods based on the MPI standard. MPI is a freely available parallel
distributed library that runs on `commodity hardware' such as a
network of workstations typically found in many office environments.
A simple demonstration indicates how one can dramatically reduce
the computational burden often associated with kernel methods thereby
achieving an almost `ideal' parallel speed-up, while the approach
is valid for multivariate kernel estimation with general bandwidths
and does not rely on approximations. Some straightforward applications
illustrate just how disarmingly simple the MPI library can be to
use.
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