Summary of Regression For Matrix-valued Data Via Kronecker Products Factorization, by Yin-jen Chen et al.
Regression for matrix-valued data via Kronecker products factorization
by Yin-Jen Chen, Minh Tang
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract proposes an algorithm called KRO-PRO-FAC for estimating parameters in matrix-variate regression problems in high-dimensional regimes. The authors utilize the Kronecker product factorization and rearrangement operations from Van Loan and Pitsianis (1993) to develop a computationally efficient method that doesn’t require estimating covariance between entries of the response variables. The paper establishes perturbation bounds for spectral norm between estimated and true parameters in sub-Gaussian settings. Numerical studies show competitive performance compared to other methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created an algorithm called KRO-PRO-FAC to help with a special kind of math problem where you’re trying to find patterns in big matrices. They used a special trick from some earlier work to make the process faster and more efficient. The team also showed that their method is good at finding the right answers, even when there’s some extra noise in the data. |
Keywords
» Artificial intelligence » Regression
