A probabilistic interpretation of sampling theory of graph signals

ICASSP 2015
Pages: 3257 - 3261
Published: Apr 19, 2015
Abstract
We give a probabilistic interpretation of sampling theory of graph signals. To do this, we first define a generative model for the data using a pairwise Gaussian random field (GRF) which depends on the graph. We show that, under certain conditions, reconstructing a graph signal from a subset of its samples by least squares is equivalent to performing MAP inference on an approximation of this GRF which has a low rank covariance matrix. We then...
Paper Details
Title
A probabilistic interpretation of sampling theory of graph signals
Published Date
Apr 19, 2015
Journal
Pages
3257 - 3261
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