A Time-Vertex Signal Processing Framework
Guardat en:
| Publicat a: | arXiv.org (May 5, 2017), p. n/a |
|---|---|
| Autor principal: | |
| Altres autors: | , , |
| Publicat: |
Cornell University Library, arXiv.org
|
| Matèries: | |
| Accés en línia: | Citation/Abstract Full text outside of ProQuest |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2074264470 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2074264470 | ||
| 045 | 0 | |b d20170505 | |
| 100 | 1 | |a Grassi, Francesco | |
| 245 | 1 | |a A Time-Vertex Signal Processing Framework | |
| 260 | |b Cornell University Library, arXiv.org |c May 5, 2017 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a 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. | |
| 653 | |a Partial differential equations | ||
| 653 | |a Digital signal processors | ||
| 653 | |a Signal processing | ||
| 653 | |a Time domain analysis | ||
| 653 | |a Video data | ||
| 653 | |a Regression analysis | ||
| 653 | |a Noise reduction | ||
| 653 | |a Harmonic analysis | ||
| 653 | |a Fourier analysis | ||
| 653 | |a Euclidean geometry | ||
| 653 | |a Seismic activity | ||
| 700 | 1 | |a Loukas, Andreas | |
| 700 | 1 | |a Perraudin, Nathanaël | |
| 700 | 1 | |a Ricaud, Benjamin | |
| 773 | 0 | |t arXiv.org |g (May 5, 2017), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2074264470/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/1705.02307 |