# Kronecker-structured Covariance Models for Multiway Data

arXiv: https://arxiv.org/abs/2212.01721 · HTML: https://mlsanity.com/paper/2212.01721/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2022.

## Built on by (most cited first, 2 of 2)

- [Higher-Order Transformers With Kronecker-Structured Attention](https://mlsanity.com/paper/2412.02919/) (2024)
- [Making Multi-Axis Gaussian Graphical Models Scalable to Millions of Cells](https://mlsanity.com/paper/2407.19892/) (2024)

## Abstract (arXiv)

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.