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arXiv · 1807.00145

Sampling and Reconstruction of Signals on Product Graphs

Guillermo Ortiz-Jiménez·Mario Coutino·Sundeep Prabhakar Chepuri·Geert Leus
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Abstract

In this paper, we consider the problem of subsampling and reconstruction of signals that reside on the vertices of a product graph, such as sensor network time series, genomic signals, or product ratings in a social network. Specifically, we leverage the product structure of the underlying domain and sample nodes from the graph factors. The proposed scheme is particularly useful for processing signals on large-scale product graphs. The sampling sets are designed using a low-complexity greedy algorithm and can be proven to be near-optimal. To illustrate the developed theory, numerical experiments based on real datasets are provided for sampling 3D dynamic point clouds and for active learning in recommender systems.

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Guillermo Ortiz-Jiménez, Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus. 2018-06-30. Sampling and Reconstruction of Signals on Product Graphs. https://arxiv.org/abs/1807.00145

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