Sampling Large Data on Graphs

Ilan Shomorony; A. Salman Avestimehr

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arXiv · 1411.3017

Sampling Large Data on Graphs

Ilan Shomorony·A. Salman Avestimehr

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Abstract

We consider the problem of sampling from data defined on the nodes of a weighted graph, where the edge weights capture the data correlation structure. As shown recently, using spectral graph theory one can define a cut-off frequency for the bandlimited graph signals that can be reconstructed from a given set of samples (i.e., graph nodes). In this work, we show how this cut-off frequency can be computed exactly. Using this characterization, we provide efficient algorithms for finding the subset of nodes of a given size with the largest cut-off frequency and for finding the smallest subset of nodes with a given cut-off frequency. In addition, we study the performance of random uniform sampling when compared to the centralized optimal sampling provided by the proposed algorithms.

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BibTeX RIS

Ilan Shomorony, A. Salman Avestimehr. 2014-11-11. Sampling Large Data on Graphs. https://arxiv.org/abs/1411.3017

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