A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is `application/pdf`

.

##
###
Estimation of Shortest Path Covariance Matrices
[article]

2020
*
arXiv
*
pre-print

We study the sample complexity of estimating the covariance matrix Σ∈ℝ^d× d of a distribution 𝒟 over ℝ^d given independent samples, under the assumption that Σ is graph-structured. In particular, we focus on shortest path covariance matrices, where the covariance between any two measurements is determined by the shortest path distance in an underlying graph with d nodes. Such matrices generalize Toeplitz and circulant covariance matrices and are widely applied in signal processing applications,

arXiv:2011.09986v1
fatcat:2mortzq7hraerhe36jkgdkfade