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Right singular vector projection graphs: fast high dimensional covariance matrix estimation under latent confounding

2020

In this work we consider the problem of estimating a high-dimensional $p \times p$ covariance matrix $\Sigma$, given $n$ observations of confounded data with covariance $\Sigma + \Gamma \Gamma^T$, where $\Gamma$ is an unknown $p \times q$ matrix of latent factor loadings. We propose a simple and scalable estimator based on the projection on to the right singular vectors of the observed data matrix, which we call RSVP. Our theoretical analysis of this method reveals that in contrast to PCA-based

doi:10.17863/cam.50715
fatcat:x2iatznlsbctfjjal6uzjvbi24