You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

View More author details

Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Label propagation

Label propagation is a family of semi-supervised algorithms that rely on the graph representation of a dataset to exploit the existing relations among nodes in order to propagate the labels to unlabeled points. 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. In the following figure, there's an example of such a structure:

Example of binary graph

The graph is defined as a structure containing two sets G = {V, E}. E is the set of vertices (or nodes) and contains all sample labels V = {–1, +1, 0}, while the edge set E is based on an affinity measure that encodes the strength of the relation between two nodes. For practical reasons, it's helpful to introduce a matrix W whose elements w_ij are: