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

01/01/2022

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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.

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

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

Related Research

Recovery of spectrum from estimated covariance matrices and statistical kernels for machine learning and big data

Blind Unwrapping of Modulo Reduced Gaussian Vectors: Recovering MSBs from LSBs

Multi-reference factor analysis: low-rank covariance estimation under unknown translations

On Robust Spectrum Sensing Using M-estimators of Covariance Matrix

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