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 ...

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