|
Abstract:
|
Many applications produce multiway data of exceedingly high dimension. Modeling such multi-way data is important in multichannel signal and video processing where sensors produce multi-indexed data, e.g. over spatial, frequency, and temporal dimensions. We will address the challenges of covariance representation of mutliway data and we will review some of the progress in statistical modeling of multiway covariance over the past two decade, focusing on tensor-valued covariance models and their inference. We will illustrate through applications to space weather, genomics and neuroscience.
Co-authors: Wayne Yu (University of Michigan), Kristjan Greenewald (IBM AI Research), Theodoris Tsiligkaridis (MIT Lincoln Laboratory)
|
