@inproceedings{083a9191d8664f13b8a2b465fe7e38ef,
title = "Variational Gaussian process for sensor fusion",
abstract = "In this paper, we introduce a new Gaussian Process (GP) classification method for multisensory data. The proposed approach can deal with noisy and missing data. It is also capable of estimating the contribution of each sensor towards the classification task. We use Bayesian modeling to build a GP-based classifier which combines the information provided by all sensors and approximates the posterior distribution of the GP using variational Bayesian inference. During its training phase, the algorithm estimates each sensor's weight and then uses this information to assign a label to each new sample. In the experimental section, we evaluate the classiication performance of the proposed method on both synthetic and real data and show its applicability to different scenarios.",
keywords = "Bayesian modeling, Gaussian process, classiication, fusion, variational inference",
author = "Neda Rohani and Pablo Ruiz and Emre Besler and Rafael Molina and Katsaggelos, {Aggelos K.}",
note = "Publisher Copyright: {\textcopyright} 2015 EURASIP.; 23rd European Signal Processing Conference, EUSIPCO 2015 ; Conference date: 31-08-2015 Through 04-09-2015",
year = "2015",
month = dec,
day = "22",
doi = "10.1109/EUSIPCO.2015.7362367",
language = "English (US)",
series = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "170--174",
booktitle = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",
address = "United States",
}