Special Issue "Information Theory and Graph Signal Processing"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editors

Guest Editor
Prof. Luis Vergara Website E-Mail
Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, 46022 València, Spain
Phone: +34 963877308
Interests: statistical signal processing; statistical signal processing; pattern recognition; machine learning; graph signal processing
Guest Editor
Dr. Addisson Salazar E-Mail
Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, 46022 València, Spain
Phone: +34 963877930
Interests: pattern recognition; signal processing on graphs; dynamic modeling; decision fusion; machine learning

Special Issue Information

Dear Colleagues,

Graph signal processing (GSP) is an emerging area of increasing interest. Essentially, the concept of a signal defined in a uniform time or space grid is extended to more general grids and domains. This dramatically opens new possibilities for the signal processing community, by establishing a bridge between signal and data processing. So, currently, many efforts are driven to define concepts, perspectives, and applications to demonstrate that GSP has its own merits regarding other related areas of data processing. Thus, for example, the classical concept of filtering has been extended to GSP by an appropriate definition of shift operators and graph eigenfunctions. Other basic concepts like stationarity have been extended in different ways yet this is still an open issue of theoretical research. All these definitions depend on the considered graph connectivity matrix (Laplacian, adjacency, etc.), hence another key aspect in GSP is the definition of the graph structure and connections. In many applications, this is made by considering context-related information which helps define pairwise connections. However, in a general setting of data processing, the connectivity should be learned from training data. Several methods to learn the graph connectivity under regularization constraints have been proposed so far. In most cases, Gaussianity is assumed to model the underlying multidimensional distributions, and partial or total correlations are considered to quantify the pairwise connections.

The main goal of this Special Issue is to contribute to progress in GSP by incorporating concepts emanating from information theory. In particular, new developments may include, but are not limited to the following:

  • Interpretation of existing concepts and methods of GSP from an information theory perspective.
  • New definitions of stationarity, localization, and uncertainty in GSP.
  • Connectivity learning: non-Gaussian models, pairwise connections based on information theory concepts.
  • New applications of GSP.

Prof. Luis Vergara
Dr. Addisson Salazar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • graph signal processing
  • information theory
  • graph statistical models
  • graph learning

Published Papers (1 paper)

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Research

Open AccessFeature PaperArticle
A New Surrogating Algorithm by the Complex Graph Fourier Transform (CGFT)
Entropy 2019, 21(8), 759; https://doi.org/10.3390/e21080759 - 04 Aug 2019
Abstract
The essential step of surrogating algorithms is phase randomizing the Fourier transform while preserving the original spectrum amplitude before computing the inverse Fourier transform. In this paper, we propose a new method which considers the graph Fourier transform. In this manner, much more [...] Read more.
The essential step of surrogating algorithms is phase randomizing the Fourier transform while preserving the original spectrum amplitude before computing the inverse Fourier transform. In this paper, we propose a new method which considers the graph Fourier transform. In this manner, much more flexibility is gained to define properties of the original graph signal which are to be preserved in the surrogates. The complex case is considered to allow unconstrained phase randomization in the transformed domain, hence we define a Hermitian Laplacian matrix that models the graph topology, whose eigenvectors form the basis of a complex graph Fourier transform. We have shown that the Hermitian Laplacian matrix may have negative eigenvalues. We also show in the paper that preserving the graph spectrum amplitude implies several invariances that can be controlled by the selected Hermitian Laplacian matrix. The interest of surrogating graph signals has been illustrated in the context of scarcity of instances in classifier training. Full article
(This article belongs to the Special Issue Information Theory and Graph Signal Processing)
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