Semi-supervised Learning

In traditional supervised learning, one uses ”labeled” data to build a model. However, labeling the training data for real-world applications is difficult, expensive, or time consuming, as it requires the effort of human annotators sometimes with specific domain experience and training. There are implicit costs associated with obtaining these labels from domain experts, such as limited time and financial resources. This is especially true for applications that involve learning with large number of class labels and sometimes with similarities among them. Semi-supervised learning (SSL) addresses this inherent bottleneck by allowing the model to integrate part or all of the available unlabeled data in its supervised learning. The goal is to maximize the learning performance of the model through such newly-labeled examples while minimizing the work required of human annotators. Exploiting unlabeled data to help improve the learning performance has become a hot topic during the last decade and it is divided into four main directions: SSL with graphs, SSL with generative models, semi-supervised support vector machines and SSL by disagreement (SSL with committees). This survey article provides an overview to research advances in this branch of machine learning.