Introduction to Semi-Supervised Learning

Semi-supervised learning is a machine learning approach that utilizes both labeled and unlabeled data to train a model, which is particularly useful when labeled data is scarce or expensive to obtain. For this method to be effective, the unlabeled data must contain useful information and adhere to key assumptions like the Smoothness, Cluster, Low-Density, and Manifold assumptions. These assumptions generally posit that similar data points should share the same label and that decision boundaries should lie in low-density regions. The algorithms are categorized as either inductive, which build a general model to predict on new data, or transductive, which directly infer labels for the provided unlabeled data, often using graph-based methods.