We’re going to delve into semi-supervised learning, a fascinating approach that bridges the gap between supervised and unsupervised learning. This method is especially useful when we have a small amount of labeled data and a large amount of unlabeled data. Let’s explore how semi-supervised learning works, its key techniques, and some practical applications.

What is Semi-Supervised Learning?

Semi-supervised learning is a hybrid approach that leverages both labeled and unlabeled data to train models. The primary idea is to use the labeled data to guide the learning process while utilizing the unlabeled data to improve the model's generalization and robustness.

Key Concepts

• Labeled Data: Data points with known output labels.
• Unlabeled Data: Data points without output labels.
• Weighting Factor: A parameter that balances the influence of labeled and unlabeled data during training.

The model is trained to optimize a loss function that incorporates both supervised learning (matching labels) and unsupervised learning (discovering patterns).

Semi-Supervised Learning Techniques

Self-Training

In self-training, the model is initially trained on the labeled data. It then predicts labels for the unlabeled data, which are treated as pseudo-labels. The model is retrained on both the labeled and pseudo-labeled data.

Co-Training

Co-training involves training two models on the same dataset but with different feature sets. Each model predicts labels for the unlabeled data, and these predictions are used to augment the training set of the other model.

Graph-Based Methods

Graph-based methods represent data points as nodes in a graph, with edges representing similarities between points. Label propagation techniques can spread label information from labeled to unlabeled nodes based on their connectivity.