Unsupervised Machine Learning
In unsupervised learning, the aim is to learn groupings in data or reduce their dimensionality. Contrary to its supervised counterpart, there are no known labels to predict. Unsupervised learning is often used for clustering analysis. Here, the algorithm aims to describe the data in a limited number of clusters or groups, where goodness-of-fit tests determine the most parsimonious model. An example30 is the discovery of asthma phenotypes based on longitudinal wheezing patterns or clinical variables. Techniques for unsupervised learning are latent class analysis (LCA), k-means clustering, principal component analysis (PCA), and Multidimensional Scaling (MDS). Recently, also semi-supervised learning has grown in popularity, which aims to overcome the lack of sufficiently large, labeled datasets and the tedious task of manual labeling. It leverages a dataset of yet unlabeled data to improve the performance of a model that is initially trained on labeled data.