Graph-Based Semi-Supervised Learning
In this chapter, we continue our discussion about semi-supervised learning, considering a family of algorithms that are based on the graph obtained from a dataset, and the existing relationships among samples. The problems that we are going to discuss belong to two main categories: the propagation of class labels to unlabeled samples, and the use of non-linear techniques based on the manifold assumption to reduce the dimensionality of the original dataset. In particular, this chapter covers the following propagation algorithms:
- Label propagation based on the weight matrix
- Label propagation in scikit-learn, based on transition probabilities
- Label spreading
- Laplacian regularization
- Propagation based on Markov ...