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Authors: Sangkyun Lee and Christian Pölitz

Affiliation: Technische Universität Dortmund, Germany

ISBN: 978-989-758-018-5

Keyword(s): Sensor Networks, Support Vector Machines, Gaussian Kernels, Sampling, Matrix Completion.

Related Ontology Subjects/Areas/Topics: Kernel Methods ; Matrix Factorization ; Pattern Recognition ; Similarity and Distance Learning ; Theory and Methods

Abstract: Recent developments in sensor technology allows for capturing dynamic patterns in vehicle movements, temperature changes, and sea-level fluctuations, just to name a few. A usual way for decision making on sensor networks, such as detecting exceptional surface level changes across the Pacific ocean, involves collecting measurement data from all sensors to build a predictor in a central processing station. However, data collection becomes challenging when communication bandwidth is limited, due to communication distance or low-energy requirements. Also, such settings will introduce unfavorable latency for making predictions on unseen events. In this paper, we propose an alternative strategy for such scenarios, aiming to build a consensus support vector machine (SVM) in each sensor station by exchanging a small amount of sampled information from local kernel matrices amongst peers. Our method is based on decomposing a “global” kernel defined with all features into “local” kernels defined only with attributes stored in each sensor station, sampling few entries of the decomposed kernel matrices that belong to other stations, and filling in unsampled entries in kernel matrices by matrix completion. Experiments on benchmark data sets illustrate that a consensus SVM can be built in each station using limited communication, which is competent in prediction performance to an SVM built with accessing all features. (More)

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Paper citation in several formats:
Lee S. and Pölitz C. (2014). Kernel Completion for Learning Consensus Support Vector Machines in Bandwidth-limited Sensor Networks.In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 113-124. DOI: 10.5220/0004829401130124

@conference{icpram14,
author={Sangkyun Lee and Christian Pölitz},
title={Kernel Completion for Learning Consensus Support Vector Machines in Bandwidth-limited Sensor Networks},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={113-124},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004829401130124},
isbn={978-989-758-018-5},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Kernel Completion for Learning Consensus Support Vector Machines in Bandwidth-limited Sensor Networks
SN - 978-989-758-018-5
AU - Lee S.
AU - Pölitz C.
PY - 2014
SP - 113
EP - 124
DO - 10.5220/0004829401130124

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