Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
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Label spreading

Another algorithm (proposed by Zhou et al.) that we need to analyze is called label spreading, which offers a slight better stability when the dataset is very noisy or dense. In these cases, standard label propagation might suffer a loss of precision due to the closeness of points with different labels. Conversely, label spreading is more robust because the Laplacian is normalized and abrupt transitions are more heavily penalized (all mathematical details are quite complex, but the reader can find all details in Biyikoglu T., Leydold J., Stadler P. F., Laplacian Eigenvectors of Graphs, Springer, 2007).

The algorithm is based on the normalized graph Laplacian, defined as:

Considering it in matrix form, it has a diagonal element equal to 1, if the degree (0 otherwise), and all the other elements equal to:

This operator is a particular case of a generic graph Laplacian:

The behavior of such an operator is analogous to a discrete Laplacian...