Semi-Supervised Learning

What is Semi-Supervised Learning? Semi-Supervised Learning Explained

Semi-supervised learning is a machine learning paradigm that falls between supervised learning and unsupervised learning. In semi-supervised learning, a training dataset contains both labeled and unlabeled examples, where labeled data has input features along with corresponding target labels, and unlabeled data only has input features.

The goal of semi-supervised learning is to leverage the additional unlabeled data to improve the performance of the learning algorithm compared to using only the labeled data. By using the unlabeled data, the algorithm can capture more underlying patterns and structure in the data, leading to better generalization and improved predictive accuracy.

Here are some key characteristics and approaches in semi-supervised learning:

Limited Labeled Data: Semi-supervised learning assumes that labeled data is expensive or time-consuming to obtain. Therefore, it seeks to make effective use of a small labeled dataset combined with a larger pool of unlabeled data.

Self-Training: One common approach in semi-supervised learning is self-training. Initially, a supervised learning algorithm is trained on the labeled data. The trained model is then used to make predictions on the unlabeled data. The most confident predictions are added to the labeled data, and the model is retrained on this augmented labeled dataset. This process of iterative labeling and retraining continues until convergence or a predetermined stopping criterion.

Co-training: Co-training is another approach in semi-supervised learning where multiple views or representations of the data are utilized. The training data is split into multiple subsets, and each subset is used to train a separate model. The models then exchange and leverage the information from their predictions on the unlabeled data, helping each other to improve their performance.

Graph-based Methods: Graph-based semi-supervised learning methods construct a graph representation of the data, where each data point is a node, and edges are defined based on similarity or proximity measures. Labeled and unlabeled data points are connected in the graph, and label information propagates through the graph to influence the labeling of unlabeled data points.

Semi-supervised learning has been applied in various domains where acquiring labeled data is costly or time-consuming, such as text classification, image recognition, and speech processing. It allows leveraging the vast amount of unlabeled data available in many real-world applications to improve learning performance. However, semi-supervised learning also comes with challenges, such as the risk of propagating errors from the initial labeled data and the need to balance the utilization of labeled and unlabeled data effectively.