Flenner

Integrating information from many different data sources to provide better situational awareness is an essential Navy issue. Many data fusion models use statistical methods to reduce statistical errors. Machine learning and big data provide, on the other hand, provides a unique framework for information fusion through our ability to learn what added benefits a different modality can provide. In this work, we provide a novel data fusion method that integrates relational data, provided to us in the form of a graph, and image data. We build an energy model that learns a representation of the data where different data sources are assumed to be similar using a graphical model. The energy model is a non-convex function which we optimize using stochastic gradient descent with momentum. The effectiveness of the model is demonstrated in an automated target recognition example.