5,289 research outputs found
Distributed Robust Learning
We propose a framework for distributed robust statistical learning on {\em
big contaminated data}. The Distributed Robust Learning (DRL) framework can
reduce the computational time of traditional robust learning methods by several
orders of magnitude. We analyze the robustness property of DRL, showing that
DRL not only preserves the robustness of the base robust learning method, but
also tolerates contaminations on a constant fraction of results from computing
nodes (node failures). More precisely, even in presence of the most adversarial
outlier distribution over computing nodes, DRL still achieves a breakdown point
of at least , where is the break down point of
corresponding centralized algorithm. This is in stark contrast with naive
division-and-averaging implementation, which may reduce the breakdown point by
a factor of when computing nodes are used. We then specialize the
DRL framework for two concrete cases: distributed robust principal component
analysis and distributed robust regression. We demonstrate the efficiency and
the robustness advantages of DRL through comprehensive simulations and
predicting image tags on a large-scale image set.Comment: 18 pages, 2 figure
Towards large scale continuous EDA: a random matrix theory perspective
Estimation of distribution algorithms (EDA) are a major branch of evolutionary algorithms (EA) with some unique advantages in principle. They are able to take advantage of correlation structure to drive the search more efficiently, and they are able to provide insights about the structure of the search space. However, model building in high dimensions is extremely challenging and as a result existing EDAs lose their strengths in large scale problems.
Large scale continuous global optimisation is key to many real world problems of modern days. Scaling up EAs to large scale problems has become one of the biggest challenges of the field.
This paper pins down some fundamental roots of the problem and makes a start at developing a new and generic framework to yield effective EDA-type algorithms for large scale continuous global optimisation problems. Our concept is to introduce an ensemble of random projections of the set of fittest search points to low dimensions as a basis for developing a new and generic divide-and-conquer methodology. This is rooted in the theory of random projections developed in theoretical computer science, and will exploit recent advances of non-asymptotic random matrix theory
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