Special Issue on Advances in Kernel-Based Learning for Signal Processing [From the Guest Editors]
he importance of learning and adaptation in statistical signal processing creates a ymbiotic relationship with machine learning. However, the two disciplines possess different momentum and emphasis, which makes it attractive to periodically review trends and new developments in their overlapping spheres of influence. Looking at the recent trends in machine learning, we see increasing interest in kernel methods, Bayesian reasoning, causality, information theoretic learning, reinforcement learning, and nonnumeric data processing, just to name a few. While some of the machine-learning community trends are clearly visible in signal processing, such as the increased popularity of the Bayesian methods and graphical models, others such as kernel approaches are still less prominent. However, kernel methods offer a number of unique advantages for signal processing, and this special issue aims to review some of those.