What is semi-supervised learning? Explain how semi-supervised learning utilizes both labeled and unlabeled data to improve model performance. Discuss the key principles behind semi-supervised learning algorithms, such as how they leverage unlabeled data to enhance the learning process and generalization of models. Additionally, highlight the advantages of semi-supervised learning in scenarios where labeled data might be limited or expensive to acquire. Can you also elaborate on challenges or considerations when applying semi-supervised learning methods, especially in domains where the quality or quantity of labeled data is scarce?

Semi-supervised learning is a machine learning paradigm that leverages both labeled and unlabeled data to train models. In most real-world scenarios, acquiring labeled data can be expensive, time-consuming, or simply difficult due to various constraints. Unlabeled data,...