# Risk of the Least Squares Minimum Norm Estimator under the Spike Covariance Model

arXiv: https://arxiv.org/abs/1912.13421 · HTML: https://mlsanity.com/paper/1912.13421/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Towards an Understanding of Benign Overfitting in Neural Networks](https://mlsanity.com/paper/2106.03212/) (2021)
- [Support vector machines and linear regression coincide with very high-dimensional features](https://mlsanity.com/paper/2105.14084/) (2021)
- [High-dimensional ridgeless least squares interpolation under spiked covariance structures](https://mlsanity.com/paper/2608.07281/) (2026)

## Abstract (arXiv)

We study risk of the minimum norm linear least squares estimator in when the number of parameters d depends on n, and fracdn rightarrow ∞. We assume that data has an underlying low rank structure by restricting ourselves to spike covariance matrices, where a fixed finite number of eigenvalues grow with n and are much larger than the rest of the eigenvalues, which are (asymptotically) in the same order. We show that in this setting risk of minimum norm least squares estimator vanishes in compare to risk of the null estimator. We give asymptotic and non asymptotic upper bounds for this risk, and also leverage the assumption of spike model to give an analysis of the bias that leads to tighter bounds in compare to previous works.