Operator analysis and diffusion based embeddings for heterogeneous data fusion

As new sensing modalities emerge and the presence of multiple sensors per platform becomes widespread, it is vital to develop new algorithms and techniques which can fuse this data. Previous attempts to deal with the problem of heterogeneous data integration for the applications in data classification were either highly data dependent or relied on simply fusing classifier outputs. In this paper we examine several related approaches: graph fusion, operator fusion, and feature space fusion. They are all associated with graph diffusion processes generated by appropriately designed operators. Our results do not make any assumptions about the data and can be easily extended to new additional modalities.