Information Theory Applications in 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 (15 January 2019) | Viewed by 71573
Special Issue Editors
Interests: signal processing; information theory; machine learning; communications; audio
Special Issues, Collections and Topics in MDPI journals
Interests: digital signal processing; biomedical engineering; digital communications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Information theory plays a fundamental role in the determination of theoretical performance limits for statistical estimation, detection, and compression. Its remarkable history of success during the last few decades has fueled research on information-guided principles for data analysis and signal processing. These dynamic and fast-growing fields have to cope with increasingly complex scenarios and novel applications in component analysis, machine learning, and communications. Hence, there is a need for specific information theoretic criteria and algorithms that work in each of the considered situations and attain a set of desired goals, for instance, an enhancement in the interpretability of the solutions, improvements in performance, robustness with respect to the model uncertainties and possible data perturbations, a reliable convergence for the algorithms and any other kind of theoretical guarantees.
In this Special Issue, we encourage researchers to present their original and recent developments in information theory for advanced methods in signal processing. Possible topics include, but are not limited to, the following:
- Information criteria, divergence measures and algorithms for source separation, independent component analysis, matrix/tensor decompositions, data approximation and completion, low-rank and sparse based methods.
- Applications in machine learning, including supervised and unsupervised methods, data representation, dimensionality reduction, feature extraction, Bayesian approaches and deep learning.
- Applications in statistical signal processing, including parameter estimation, system identification, pattern classification, signal approximation and compressed sensing, signal analysis and restoration.
- Applications in biomedical engineering, speech/audio processing, and communications.
Dr. Sergio Cruces
Dr. Rubén Martín-Clemente
Dr. Wojciech Samek
Guest Editors
Manuscript Submission Information
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