Wavelet-Based Visual Analysis for Data Exploration

Alcebiades Dal Col; Paola Valdivia; Fabiano Petronetto; Fabio DIas; Claudio T. Silva; L. Gustavo Nonato

doi:10.1109/MCSE.2017.3421553

Wavelet-Based Visual Analysis for Data Exploration

Alcebiades Dal Col, Paola Valdivia, Fabiano Petronetto, Fabio DIas, Claudio T. Silva, L. Gustavo Nonato

Urban Initiative

Research output: Contribution to journal › Article › peer-review

Abstract

The conventional wavelet transform is widely used in image and signal processing, where a signal is decomposed into a combination of known signals. By analyzing the individual contributions, the behavior of the original signal can be inferred. In this article, the authors present an introductory overview of the extension of this theory into graphs domains. They review the graph Fourier transform and graph wavelet transforms that are based on dictionaries of graph spectral filters, namely, spectral graph wavelet transforms. Then, the main features of the graph wavelet transforms are presented using real and synthetic data.

Original language	English (US)
Article number	8024139
Pages (from-to)	85-91
Number of pages	7
Journal	Computing in Science and Engineering
Volume	19
Issue number	5
DOIs	https://doi.org/10.1109/MCSE.2017.3421553
State	Published - 2017

Keywords

graph signal processing
scientific computing
spectral graph wavelet transforms
time-varying data

ASJC Scopus subject areas

Computer Science(all)
Engineering(all)

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10.1109/MCSE.2017.3421553

Cite this

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title = "Wavelet-Based Visual Analysis for Data Exploration",

abstract = "The conventional wavelet transform is widely used in image and signal processing, where a signal is decomposed into a combination of known signals. By analyzing the individual contributions, the behavior of the original signal can be inferred. In this article, the authors present an introductory overview of the extension of this theory into graphs domains. They review the graph Fourier transform and graph wavelet transforms that are based on dictionaries of graph spectral filters, namely, spectral graph wavelet transforms. Then, the main features of the graph wavelet transforms are presented using real and synthetic data.",

keywords = "graph signal processing, scientific computing, spectral graph wavelet transforms, time-varying data",

author = "{Dal Col}, Alcebiades and Paola Valdivia and Fabiano Petronetto and Fabio DIas and Silva, {Claudio T.} and Nonato, {L. Gustavo}",

note = "Funding Information: Grants 2015/03330-7, 2014/12815-1, 2013/14089-3, 2016/04391-2, 2016/04190-7, and 2011/22749-8 from Sao Paulo Research Foundation (FAPESP). The views expressed are those of the authors and do not reflect the official policy or position of the Sao Paulo Research Foundation. Publisher Copyright: {\textcopyright} 2017 IEEE.",

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doi = "10.1109/MCSE.2017.3421553",

language = "English (US)",

volume = "19",

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AU - Valdivia, Paola

AU - Petronetto, Fabiano

AU - DIas, Fabio

AU - Silva, Claudio T.

AU - Nonato, L. Gustavo

N1 - Funding Information: Grants 2015/03330-7, 2014/12815-1, 2013/14089-3, 2016/04391-2, 2016/04190-7, and 2011/22749-8 from Sao Paulo Research Foundation (FAPESP). The views expressed are those of the authors and do not reflect the official policy or position of the Sao Paulo Research Foundation. Publisher Copyright: © 2017 IEEE.

PY - 2017

Y1 - 2017

N2 - The conventional wavelet transform is widely used in image and signal processing, where a signal is decomposed into a combination of known signals. By analyzing the individual contributions, the behavior of the original signal can be inferred. In this article, the authors present an introductory overview of the extension of this theory into graphs domains. They review the graph Fourier transform and graph wavelet transforms that are based on dictionaries of graph spectral filters, namely, spectral graph wavelet transforms. Then, the main features of the graph wavelet transforms are presented using real and synthetic data.

AB - The conventional wavelet transform is widely used in image and signal processing, where a signal is decomposed into a combination of known signals. By analyzing the individual contributions, the behavior of the original signal can be inferred. In this article, the authors present an introductory overview of the extension of this theory into graphs domains. They review the graph Fourier transform and graph wavelet transforms that are based on dictionaries of graph spectral filters, namely, spectral graph wavelet transforms. Then, the main features of the graph wavelet transforms are presented using real and synthetic data.

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KW - scientific computing

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Wavelet-Based Visual Analysis for Data Exploration

Abstract

Keywords

ASJC Scopus subject areas

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