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.
Original language | English |
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Title of host publication | 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) |
Subtitle of host publication | Proceedings |
Editors | Shuguang Cui, Hamid Jafarkhani |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 713-717 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-7281-1295-4 |
ISBN (Print) | 978-1-7281-1296-1 |
DOIs | |
Publication status | Published - 2018 |
Event | 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States Duration: 26 Nov 2018 → 29 Nov 2018 |
Conference
Conference | 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 |
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Country/Territory | United States |
City | Anaheim |
Period | 26/11/18 → 29/11/18 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Active learning
- Graph signal processing
- Product graphs
- Sparse sampling
- Submodularity