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Mastering Machine Learning Algorithms

You're reading from   Mastering Machine Learning Algorithms Expert techniques to implement popular machine learning algorithms and fine-tune your models

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788621113
Length 576 pages
Edition 1st Edition
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Author (1):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (17) Chapters Close

Preface 1. Machine Learning Model Fundamentals 2. Introduction to Semi-Supervised Learning FREE CHAPTER 3. Graph-Based Semi-Supervised Learning 4. Bayesian Networks and Hidden Markov Models 5. EM Algorithm and Applications 6. Hebbian Learning and Self-Organizing Maps 7. Clustering Algorithms 8. Ensemble Learning 9. Neural Networks for Machine Learning 10. Advanced Neural Models 11. Autoencoders 12. Generative Adversarial Networks 13. Deep Belief Networks 14. Introduction to Reinforcement Learning 15. Advanced Policy Estimation Algorithms 16. Other Books You May Enjoy

Label spreading

The last algorithm (proposed by Zhou et al.) that we need to analyze is called label spreading, and it's based on the normalized graph Laplacian:

This matrix has each a diagonal element lii equal to 1, if the degree deg(lii) > 0 (0 otherwise) and all the other elements equal to:

The behavior of this matrix is analogous to a discrete Laplacian operator, whose real-value version is the fundamental element of all diffusion equations. To better understand this concept, let's consider the generic heat equation:

This equation describes the behavior of the temperature of a room when a point is suddenly heated. From basic physics concepts, we know that heat will spread until the temperature reaches an equilibrium point and the speed of variation is proportional to the Laplacian of the distribution. If we consider a bidimensional grid at the equilibrium...

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