Low-rank MMSE filters, Kronecker-product representation, and regularization: a new perspective
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Abstract (arXiv)
In this work, we propose a method to efficiently find the regularization parameter for low-rank MMSE filters based on a Kronecker-product representation. We show that the regularization parameter is surprisingly linked to the problem of rank selection and, thus, properly choosing it, is crucial for low-rank settings. The proposed method is validated through simulations, showing significant gains over commonly used methods.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 26 references that are in the map. First posted on arXiv 16 Dec 2025.
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References · 6
- 2014Generalized Low Rank Modelscited by 55
- 2013Tucker Tensor Regression and Neuroimaging Analysiscited by 26
- 2015Bayesian Tensor Regressioncited by 21
- 2024Nuclear Norm Regularization for Deep Learningcited by 11
- 2023Tensor Regressioncited by 4
- 2023Automatic Regularization for Linear MMSE Filterscited by 1
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