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: closed (17 July 2020) | Viewed by 14921
Special Issue Editors
Interests: statistical signal processing; pattern recognition; machine learning; graph signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: pattern recognition; signal processing on graphs; dynamic modeling; decision fusion; machine learning
Special Issues, Collections and Topics in MDPI journals
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
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Keywords
- graph signal processing
- information theory
- graph statistical models
- graph learning
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