Unsupervised and Semisupervised Learning

Abstract

Methodologies for unsupervised and semisupervised learning are reviewed. For unsupervised learning, or clustering, the focus is on mixture‐model‐based approaches under both the classic and mode association frameworks. High‐dimensional data pose a major challenge for clustering. We thus discuss in detail variable selection and the hidden Markov model on variable blocks, which exploits a graph structure to simplify the dependence among variables. We also present topics that emerged relatively recently such as clustering distributional data under the Wasserstein metric and uncertainty assessment for cluster analysis. Semisupervised learning has attracted growing interest in the machine learning community in recent years. We review foundational approaches including self‐training, semisupervised generative models, and graphical models. We then describe in greater depth entropy minimization, consistency regularization, and mixup augmentation, methods that are utilized in state‐of‐the‐art models such as MixMatch.