# Recover the spectrum of covariance matrix: a non-asymptotic iterative method

arXiv: https://arxiv.org/abs/2201.00230 · HTML: https://mlsanity.com/paper/2201.00230/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 1 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [An Overview on the Estimation of Large Covariance and Precision Matrices](https://mlsanity.com/paper/1504.02995/) (2015)
- [Spectrum Estimation from Samples](https://mlsanity.com/paper/1602.00061/) (2016)
- [Estimation of the population spectral distribution from a large dimensional sample covariance matrix](https://mlsanity.com/paper/1302.0355/) (2013)

## Abstract (arXiv)

It is well known the sample covariance has a consistent bias in the spectrum, for example spectrum of Wishart matrix follows the Marchenko-Pastur law. We in this work introduce an iterative algorithm 'Concent' that actively eliminate this bias and recover the true spectrum for small and moderate dimensions.