Matrix and Tensor Factorizations for Data Fusion: Focus on Model Match, Interpretability, and Reproducibility

Tülay Adali
University of Maryland Baltimore County

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 the main models that have been successfully used for fusion of multiple datasets.
Examples based on independent component and independent vector analysis as well as canonical polyadic decomposition are discussed in more detail with examples in fusion of neuroimaging data. Importance of computational reproducibility is also addressed, with a focus on its relationship to model match and interpretability.

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