Code Supplement for "ScreeNOT: Exact MSE-Optimal Singular Value Thresholding in Correlated Noise"
We derive a formula for optimal hard thresholding of the singular value decomposition in the presence of correlated additive noise; although it nominally involves unobservables, we show how to apply it even where the noise covariance structure is not a-priori known or is not independently estimable.
The proposed method, which we call ScreeNOT, is a mathematically solid alternative to Cattell's ever-popular but vague Scree Plot heuristic from 1966.
ScreeNOT has a surprising oracle property: it typically achieves exactly, in large finite samples, the lowest possible MSE for matrix recovery, on each given problem instance - i.e. the specific threshold it selects gives exactly the smallest achievable MSE loss among all possible threshold choices for that noisy dataset and that unknown underlying true low rank model. The method is computationally efficient and robust against perturbations of the underlying covariance structure.
Our results depend on the assumption that the singular values of the noise have a limiting empirical distribution of compact support; this model, which is standard in random matrix theory, is satisfied by many models exhibiting either cross-row correlation structure or cross-column correlation structure, and also by many situations where there is inter-element correlation structure. Simulations demonstrate the effectiveness of the method even at moderate matrix sizes. The paper is supplemented by ready-to-use software packages implementing the proposed algorithm.
|Type of resource
|January 10, 2023
|September 29, 2020
|Donoho, David L.
|Singular value thresholding
|Low-rank matrix denoising
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- This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).
- Preferred citation
- Donoho, David L., Gavish, Matan and Romanov, Elad. (2020). Code Supplement for "ScreeNOT: Exact MSE-Optimal Singular Value Thresholding in Correlated Noise". Stanford Digital Repository. Available online at: https://purl.stanford.edu/py196rk3919
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