Research Repository

Sparse Gaussian Process for Spatial Function Estimation with Mobile Sensor Networks

Lu, Bowen and Gu, Dongbing and Hu, Huosheng and McDonald-Maier, Klaus (2012) Sparse Gaussian Process for Spatial Function Estimation with Mobile Sensor Networks. In: 2012 Third International Conference on Emerging Security Technologies (EST), 2012-09-05 - 2012-09-07, Lisbon, Portugal.

[img]
Preview
Text
Sparse Gaussian.pdf - Accepted Version

Download (1MB) | Preview

Abstract

Gaussian process (GP) is well researched and used in machine learning field. Comparing with artificial neural network (ANN) and support vector regression (SVR), it provides additional covariance information for regression results. By exploiting this feature, an uncertainty based locational optimisation strategy combining with an entropy based data selection method for mobile sensor networks is presented in this paper. Centroidal Voronoi tessellation (CVT) is used as a locational optimisation framework and Informative Vector Machine (IVM) is applied for data selection. Simulations with different locational optimisation criteria are conducted and the results are given, which proved the effectiveness of presented strategy.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2012 Third International Conference on Emerging Security Technologies
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 16 Jan 2015 15:49
Last Modified: 16 Jun 2020 12:41
URI: http://repository.essex.ac.uk/id/eprint/9224

Actions (login required)

View Item View Item