CIF: Small: Source Separation with an Adaptive Structure for Multi-Modal Data Fusion

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In many fields today, information about a given phenomenon is obtained through different types of acquisition techniques and experimental conditions, and the availability of such multimodal data has been growing. Joint analysis of this data---its fusion---promises a more comprehensive and informative view of the task at hand, and, if probed carefully, may open new venues and answer questions we might not have even thought of asking when working with a single modality. There is now significant activity in data fusion in disciplines spanning medical imaging, remote sensing, speech processing, behavioral sciences, and metabolomics, to name a few. A common challenge across multiple disciplines is determining how and to what degree different datasets are related, i.e., identifying the common and distinct subspaces across multiple datasets in terms of the information they provide for the given task.The current work provides a powerful solution to this key challenge enabling identification of the relationship among multiple datasets in a completely data-driven manner such that both the common and distinct subspaces within each dataset can be robustly identified. By combining this adaptive scheme with the well defined but flexible framework of independent vector analysis (IVA), a new powerful framework, IVA with an adaptive subspace structure (IVA-AS) is developed for effective fusion of both the multiset and multimodal data. The successful application of this framework is demonstrated using a unique medical dataset that allows the study of commonalities and differences in brain function while driving with distractions or under the influence of alcohol.