We consider the problem of approximating the set of eigenvalues of the covariance matrix of a multivariate distribution (equivalently, the problem of approximating the "population spectrum"), given access to samples drawn from the distribution.
Jan 30, 2016
We propose a theoretically optimal and computationally efficient algorithm for recovering the moments of the eigenvalues of the population covariance matrix.
We consider this recovery problem in the regime where the sample size is comparable to, or even sublinear in the dimensionality of the distribution. First, we ...
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To what extent can the eigenvalues of the true distribution (the population spectrum) be accurately recovered from the samples, particularly in the “sublinear” ...
This work proposes a theoretically optimal and computationally efficient algorithm for recovering the moments of the eigenvalues of the population ...
In this paper, we address the problem of directly recovering the spectrum, which is the set of singular values, and also in sample-efficient approaches for ...
Oct 1, 2017 · Kong, Weihao, and Valiant, Gregory. "Spectrum estimation from samples". The Annals of Statistics 45 (5). Country unknown/Code not available.
A key tool to this end is consistent estimation of the set of eigenvalues of the population covariance matrix (also known as the spectrum).
Techniques for reliably estimating the power spectral density function for both small and large samples of a stationary stochastic process are described.
In this paper we analyse a class of DASP (Digital Alias-free Signal Processing) methods for spectrum estimation of sampled signals. These methods consist in ...