Adaptive Signal Processing and Machine Learning Using Entropy and Information Theory

Dear Colleagues,

Adaptive signal processing, machine learning, and deep learning which rely on the paradigm of learning from data have become indispensable tools for extracting information, making decisions, and interacting with our environment. The information extraction process is a very critical step in this process. Many of the algorithms deployed for information extraction have largely been based on using the popular mean square error (MSE) criterion. They leverage the significant information contained in the data. The more accurate the process of extracting useful information from the data, the more precise and efficient the learning and signal processing will be.

However, traditional mean square error (MSE) is not the optimal cost measure to use, when the error distribution is non-Gaussian, such as in supervised learning. In such cases, information theoretic learning (ITL)-based cost measures can provide better nonlinear models in a range of problems from system identification and regression to classification. Information theoretic learning (ITL) has initially been applied for such supervised learning applications.

Entropy and information theory have always represented useful tools to deal with information and the amount of information contained in a random variable. Information theory mainly relies on the basic intuition that learning that an unlikely event has occurred is more informative than learning that a likely event has occurred. Entropy gives a measure of the amount of information in an event drawn from a distribution. For this reason, they have been widely used in adaptive signal processing and machine learning to improve performance by designing and optimizing effective and specific models that fit the data even in noisy and adverse scenario conditions.

The presence of strong disturbances in the error signal can severely deteriorate convergence behavior of adaptive filters and in some cases cause the learning algorithms to diverge. Information theoretic learning (ITL) approaches have recently emerged as an effective solution to handle such scenarios.

Examples of several widely adopted measures include mutual information, cross-entropy, minimum error entropy (MEE) criterion, maximum correntropy criterion (MCC) and Kullback–Leibler divergence, among others. Moreover, a wide class of interesting tasks of adaptive signal processing, machine learning, and deep learning take advantage of entropy and information theory, including exploratory data analysis, feature and model selection, sampling and subset extraction, optimizing learning algorithms, clustering sensitivity analysis, representation learning, and data generation.

This Special Issue aims at providing recent developments in the areas of adaptive signal processing, machine learning, and deep learning using information theory and entropy to improve performance in widespread and popular problems and also to provide effective solutions to emerging problems.

The scope of the Special Issue includes theoretical and applications papers pertaining to all problems involving learning from data.

Prof. Dr. Tokunbo Ogunfunmi
Dr. David Luengo
Dr. Nithin V George
Dr. Danilo Comminiello
Guest Editors

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