Label propagation

Label propagation is a family of semi-supervised algorithms based on a graph representation of the dataset. In particular, if we have N labeled points (with bipolar labels +1 and -1) and M unlabeled points (denoted by y=0), it's possible to build an undirected graph based on a measure of geometric affinity among samples. If G = {V, E} is the formal definition of the graph, the set of vertices is made up of sample labels V = { -1, +1, 0 }, while the edge set is based on an affinity matrix W (often called adjacency matrix when the graph is unweighted), which depends only on the X values, not on the labels.

In the following graph, there's an example of such a structure:

Example of binary graph

In the preceding example graph, ...

Get Mastering Machine Learning Algorithms now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.