A Time-Vertex Signal Processing Framework
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| Опубліковано в:: | arXiv.org (May 5, 2017), p. n/a |
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| Автор: | |
| Інші автори: | , , |
| Опубліковано: |
Cornell University Library, arXiv.org
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| Предмети: | |
| Онлайн доступ: | Citation/Abstract Full text outside of ProQuest |
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| Короткий огляд: | An emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate the notion of joint harmonic analysis to a full-fledged framework denoted as Time-Vertex Signal Processing, that links together the time-domain signal processing techniques with the new tools of graph signal processing. This entails three main contributions: (a) We provide a formal motivation for harmonic time-vertex analysis as an analysis tool for the state evolution of simple Partial Differential Equations on graphs. (b) We improve the accuracy of joint filtering operators by up-to two orders of magnitude. (c) Using our joint filters, we construct time-vertex dictionaries analyzing the different scales and the local time-frequency content of a signal. The utility of our tools is illustrated in numerous applications and datasets, such as dynamic mesh denoising and classification, still-video inpainting, and source localization in seismic events. Our results suggest that joint analysis of time-vertex signals can bring benefits to regression and learning. |
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| ISSN: | 2331-8422 |
| Джерело: | Engineering Database |