206,279 research outputs found
Efficient Algorithms for the Data Exchange Problem
In this paper we study the data exchange problem where a set of users is
interested in gaining access to a common file, but where each has only partial
knowledge about it as side-information. Assuming that the file is broken into
packets, the side-information considered is in the form of linear combinations
of the file packets. Given that the collective information of all the users is
sufficient to allow recovery of the entire file, the goal is for each user to
gain access to the file while minimizing some communication cost. We assume
that users can communicate over a noiseless broadcast channel, and that the
communication cost is a sum of each user's cost function over the number of
bits it transmits. For instance, the communication cost could simply be the
total number of bits that needs to be transmitted. In the most general case
studied in this paper, each user can have any arbitrary convex cost function.
We provide deterministic, polynomial-time algorithms (in the number of users
and packets) which find an optimal communication scheme that minimizes the
communication cost. To further lower the complexity, we also propose a simple
randomized algorithm inspired by our deterministic algorithm which is based on
a random linear network coding scheme.Comment: submitted to Transactions on Information Theor
Near-Optimal Budgeted Data Exchange for Distributed Loop Closure Detection
Inter-robot loop closure detection is a core problem in collaborative SLAM
(CSLAM). Establishing inter-robot loop closures is a resource-demanding
process, during which robots must consume a substantial amount of
mission-critical resources (e.g., battery and bandwidth) to exchange sensory
data. However, even with the most resource-efficient techniques, the resources
available onboard may be insufficient for verifying every potential loop
closure. This work addresses this critical challenge by proposing a
resource-adaptive framework for distributed loop closure detection. We seek to
maximize task-oriented objectives subject to a budget constraint on total data
transmission. This problem is in general NP-hard. We approach this problem from
different perspectives and leverage existing results on monotone submodular
maximization to provide efficient approximation algorithms with performance
guarantees. The proposed approach is extensively evaluated using the KITTI
odometry benchmark dataset and synthetic Manhattan-like datasets.Comment: RSS 2018 Extended Versio
Computing on Vertices in Data Mining
The main challenges in data mining are related to large, multi-dimensional data sets. There is a need to develop algorithms that are precise and efficient enough to deal with big data problems. The Simplex algorithm from linear programming can be seen as an example of a successful big data problem solving tool. According to the fundamental theorem of linear programming the solution of the optimization problem can found in one of the vertices in the parameter space. The basis exchange algorithms also search for the optimal solution among finite number of the vertices in the parameter space. Basis exchange algorithms enable the design of complex layers of classifiers or predictive models based on a small number of multivariate data vectors
Mining XML documents with association rule algorithms
Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2008Includes bibliographical references (leaves: 59-63)Text in English; Abstract: Turkish and Englishx, 63 leavesFollowing the increasing use of XML technology for data storage and data exchange between applications, the subject of mining XML documents has become more researchable and important topic. In this study, we considered the problem of Mining Association Rules between items in XML document. The principal purpose of this study is applying association rule algorithms directly to the XML documents with using XQuery which is a functional expression language that can be used to query or process XML data. We used three different algorithms; Apriori, AprioriTid and High Efficient AprioriTid. We give comparisons of mining times of these three apriori-like algorithms on XML documents using different support levels, different datasets and different dataset sizes
A Randomized Exchange Algorithm for Computing Optimal Approximate Designs of Experiments
We propose a class of subspace ascent methods for computing optimal
approximate designs that covers both existing as well as new and more efficient
algorithms. Within this class of methods, we construct a simple, randomized
exchange algorithm (REX). Numerical comparisons suggest that the performance of
REX is comparable or superior to the performance of state-of-the-art methods
across a broad range of problem structures and sizes. We focus on the most
commonly used criterion of D-optimality that also has applications beyond
experimental design, such as the construction of the minimum volume ellipsoid
containing a given set of data-points. For D-optimality, we prove that the
proposed algorithm converges to the optimum. We also provide formulas for the
optimal exchange of weights in the case of the criterion of A-optimality. These
formulas enable one to use REX for computing A-optimal and I-optimal designs.Comment: 23 pages, 2 figure
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