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Covariance matrix estimates are an essential part of many signal processing algorithms, and are often used to determine a low-dimensional principal subspace via their spectral decomposition. However, for sufficiently high-dimensional matrices exact eigen-analysis is computationally intractable, and in the case of limited data, sample eigenvalues and eigenvectors are known to be poor estimators of...
Spectral methods requiring the computation of eigenvalues and eigenvectors of a positive definite matrix are an essential part of signal processing. However, for sufficiently high-dimensional data sets, the eigenvalue problem cannot be solved without approximate methods. We examine a technique for approximate spectral analysis and low-rank matrix reconstruction known as the Nyström method, which...
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