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Kronecker-structured covariance models for multiway data
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DOI：10.1214/22-SS139.png)
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Abstract
En 中文
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 multiway data and review some of the progress in statistical modeling of multiway covariance over the past two decades, focusing on tensor-valued covariance models and their inference. We will illustrate through a space weather application: predicting the evolution of solar active regions over time.
Keywords:
Tensor valued data
multiway graphical Lasso
high dimensional statistics
space weather applications
Journal
S
IF:
15.4
Papers:
30
Citations:
1.0K