Data Fusion Using Independent Vector Analysis: Solutions, Challenges, and Opportunities

Abstract

In many fields today, such as neuroscience, remote sensing, computational social science, and physical sciences, multiple sets of data are readily available. Matrix and tensor factorizations enable joint analysis, i.e., fusion, of these multiple datasets such that they can fully interact and inform each other while also minimizing the assumptions placed on their inherent relationships. A key advantage of these methods is the direct interpretability of their results. This talk presents an overview of models based on independent component analysis (ICA), and its generalization to multiple datasets, independent vector analysis (IVA) with examples using neuroimaging data. A number of important challenges and future directions of research are addressed for solutions using not only ICA and IVA but also tensors and other matrix factorizations.