mlsanityData to 7 Oct 2026

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

arXiv 2201.00230 ↗First posted 1 Jan 2022stat.ML cs.LG stat.COcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 19 references that are in the map. First posted on arXiv 1 Jan 2022.

Builds on

3 of 19 references.

References · 3

  1. 2015An Overview on the Estimation of Large Covariance and Precision Matricescited by 20
  2. 2016Spectrum Estimation from Samplescited by 9
  3. 2013Estimation of the population spectral distribution from a large dimensional sample covariance matrixcited by 1

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