Mastering Machine Learning Algorithms

Label propagation based on Markov random walks

The goal of this algorithm proposed by Zhu and Ghahramani is to find the probability distribution of target labels for unlabeled samples given a mixed dataset. This objective is achieved through the simulation of a stochastic process, where each unlabeled sample walks through the graph until it reaches a stationary absorbing state, a labeled sample where it stops acquiring the corresponding label. The main difference with other similar approaches is that in this case, we consider the probability of reaching a labeled sample. In this way, the problem acquires a closed form and can be easily solved.

The first step is to always build a k-nearest neighbors graph with all N samples, and define a weight ...