Statistical Signal Processing, Detection and Estimation: Dealing with the Data Deluge
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 5599
Special Issue Editors
Interests: statistical signal processing; digital communications; error correction coding; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: variational bayes; compressive sensing; statistical signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The present deluge of data and the ubiquity of information appliances provides opportunity and motivation for continued advances in statistical techniques to extract useful information from the deluge. These techniques may be adapted to problems related to the nature of the data, such as streaming data (with a concomitant need to update distribution estimates with low complexity), or the fact that the data may be textual or categorical. Detection and estimation theory draws elements from classical statistics (such as hypothesis testing), statistical signal processing, pattern recognition, and machine learning. These disciplines combine with techniques from a variety of application areas, such as tracking, navigation, financial modeling, bioinformatics, cybersecurity, and many others, to provide a broad and powerful set of tools. Within this toolkit, detection and estimation techniques often employ information-theoretic techniques, such as the Kullback–Leibler distortion (used, for example, in variational Bayes, or for characterizing the performance of a distribution-based classifier).
This Special Issue of Entropy aims to be a forum for the presentation of new, improved, and developing techniques in the broad area of detection and estimation theory, and applications of these techniques, particularly methods having a connection with the “entropic” theme of the journal.
This Special Issue will accept unpublished original papers and comprehensive reviews with topics related to the following, or related, areas:
- Online, incremental updates and learning methods;
- Detection and estimation on graphs;
- Variational and message passing methods;
- Machine learning, including updating models from streaming data;
- Addressing the “big p, small n” problem, dealing with high-dimensional data with relatively small amounts of learning instances;
- Detection, estimation, and tracking;
- Natural language and textual processing;
- Techniques to deal with categorical data;
- Statistical modeling techniques;
- Applications of modern detection and estimation techniques to pertinent datasets;
- Detection and estimation applied to engineered signals, such as communication signals;
- Detection and estimation under sparsity conditions.
Prof. Dr. Todd K. Moon
Dr. Mohammad Shekaramiz
Dr. Rodrigo de Lamare
Guest Editors
Manuscript Submission Information
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