Handle URI:
http://hdl.handle.net/10754/626703
Title:
Graph Sampling for Covariance Estimation
Authors:
Chepuri, Sundeep Prabhakar; Leus, Geert
Abstract:
In this paper the focus is on subsampling as well as reconstructing the second-order statistics of signals residing on nodes of arbitrary undirected graphs. Second-order stationary graph signals may be obtained by graph filtering zero-mean white noise and they admit a well-defined power spectrum whose shape is determined by the frequency response of the graph filter. Estimating the graph power spectrum forms an important component of stationary graph signal processing and related inference tasks such as Wiener prediction or inpainting on graphs. The central result of this paper is that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the second-order statistics of the graph signal from the subsampled observations, and more importantly, without any spectral priors. To this end, both a nonparametric approach as well as parametric approaches including moving average and autoregressive models for the graph power spectrum are considered. The results specialize for undirected circulant graphs in that the graph nodes leading to the best compression rates are given by the so-called minimal sparse rulers. A near-optimal greedy algorithm is developed to design the subsampling scheme for the non-parametric and the moving average models, whereas a particular subsampling scheme that allows linear estimation for the autoregressive model is proposed. Numerical experiments on synthetic as well as real datasets related to climatology and processing handwritten digits are provided to demonstrate the developed theory.
Publisher:
arXiv
KAUST Grant Number:
OSR-2015-Sensors-2700
Issue Date:
25-Apr-2017
ARXIV:
arXiv:1704.07661
Type:
Preprint
Additional Links:
http://arxiv.org/abs/1704.07661v1; http://arxiv.org/pdf/1704.07661v1
Appears in Collections:
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Full metadata record

DC FieldValue Language
dc.contributor.authorChepuri, Sundeep Prabhakaren
dc.contributor.authorLeus, Geerten
dc.date.accessioned2018-01-04T07:51:40Z-
dc.date.available2018-01-04T07:51:40Z-
dc.date.issued2017-04-25en
dc.identifier.urihttp://hdl.handle.net/10754/626703-
dc.description.abstractIn this paper the focus is on subsampling as well as reconstructing the second-order statistics of signals residing on nodes of arbitrary undirected graphs. Second-order stationary graph signals may be obtained by graph filtering zero-mean white noise and they admit a well-defined power spectrum whose shape is determined by the frequency response of the graph filter. Estimating the graph power spectrum forms an important component of stationary graph signal processing and related inference tasks such as Wiener prediction or inpainting on graphs. The central result of this paper is that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the second-order statistics of the graph signal from the subsampled observations, and more importantly, without any spectral priors. To this end, both a nonparametric approach as well as parametric approaches including moving average and autoregressive models for the graph power spectrum are considered. The results specialize for undirected circulant graphs in that the graph nodes leading to the best compression rates are given by the so-called minimal sparse rulers. A near-optimal greedy algorithm is developed to design the subsampling scheme for the non-parametric and the moving average models, whereas a particular subsampling scheme that allows linear estimation for the autoregressive model is proposed. Numerical experiments on synthetic as well as real datasets related to climatology and processing handwritten digits are provided to demonstrate the developed theory.en
dc.publisherarXiven
dc.relation.urlhttp://arxiv.org/abs/1704.07661v1en
dc.relation.urlhttp://arxiv.org/pdf/1704.07661v1en
dc.titleGraph Sampling for Covariance Estimationen
dc.typePreprinten
dc.contributor.institutionFaculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, The Netherlands.en
dc.identifier.arxividarXiv:1704.07661en
kaust.grant.numberOSR-2015-Sensors-2700en
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