Variational Gaussian process for multisensor classification problems

Abstract

This paper proposes a new model for multi-sensory data classification. To tackle this problem, probabilistic modeling and variational Bayesian inference are used. A Gaussian Process (GP) classifier is built upon the introduced modeling. Its posterior distribution is approximated using variational Bayesian inference. Finally, labels of test samples are predicted employing this classifier. Very importantly, and in contrast to alternative approaches, the proposed method does not discard samples with missing features and utilizes all available information for training. Furthermore, to take into account that the quality of the information provided by each sensor may differ (some modalities/sensors may provide more reliable/distinctive information than others), we introduce two versions of the algorithm. In the first one, the parameters modeling each sensor performance are shared while in the second one, each sensor parameters are estimated independently. Synthetic and real datasets are utilized to examine the validity of the proposed models. The results obtained for binary classification problems justify their use and confirm their superiority over existing fusion architectures.