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)
26
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27
Index

Factor Analysis

Let's suppose we have a Gaussian data-generating process , and M n-dimensional zero-centered samples drawn from it:

If pdata has a mean , it's also possible to use this model, but it's necessary to account for this non-null value with slight changes to some of the formulas. As the zero-centering normally has no drawbacks, it's easier to remove the mean to simplify the model.

One of the most common problems in unsupervised learning is finding a lower dimensional distribution plower such that the Kullback-Leibler divergence with pdata is minimized. When performing a factor analysis (FA), following the original proposal published in Rubin D., Thayer D., EM algorithms for ML factor analysis, Psychometrika, 47/1982, Issue 1, and Ghahramani Z., Hinton G. E., The EM algorithm for Mixtures of Factor Analyzers, CRC-TG-96-1, 05/1996, we start from the assumption that we can model the generic data point as a linear combination of Gaussian latent...