Model Complexity, Regularization, and Sparsity [Guest Editorial]

The articles in this special section focus on learning adaptive models. Over the past few years, sparsity has become one of the most widely used and successful forms of regularization for learning adaptive representations for descriptive and discriminative tasks. One of the most prominent and successful forms of regularization is based on the sparsity prior, which promotes solutions that can be expressed as a linear combination of only a few atoms belonging to a dictionary. Sparsity has become one of the leading approaches for learning adaptive representations for both descriptive and discriminative tasks, and has been shown to be particularly effective when dealing with structured, complex and high-dimensional data, in numerous fields including statistics, signal processing and computational intelligence.