Gleb Beliakov
Robust artificial neural networks and outlier detection. Technical report.
Tue, 10/04/2011 - 15:01 — SomNambulAuthors: Gleb Beliakov, Andrei Kelarev, John Yearwood
Subjects: Optimization and Control
AbstractLarge outliers break down linear and nonlinear regression models. Robust
regression methods allow one to filter out the outliers when building a model.
By replacing the traditional least squares criterion with the least trimmed
squares criterion, in which half of data is treated as potential outliers, one
can fit accurate regression models to strongly contaminated data.
High-breakdown methods have become very well established in linear regression,
but have started being applied for non-linear regression only recently.Parallel calculation of the median and order statistics on GPUs with application to robust regression.
Fri, 04/15/2011 - 15:01 — SomNambulAuthors: Gleb Beliakov
Subjects: and Cluster Computing, Distributed, Parallel
AbstractWe present and compare various approaches to a classical selection problem on
Graphics Processing Units (GPUs). The selection problem consists in selecting
the $k$-th smallest element from an array of size $n$, called $k$-th order
statistic. We focus on calculating the median of a sample, the $n/2$-th order
statistic. We introduce a new method based on minimization of a convex
function, and show its numerical superiority when calculating the order
statistics of very large arrays on GPUs.
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