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

Independent Component Analysis

We've seen that the factors extracted by a PCA are decorrelated, but not independent. A classic example is a cocktail party: we have a recording of many overlapped voices and we would like to separate them. Every single voice can be modeled as a random process and it's possible to assume that they are statistically independent (this means that the joint probability can be factorized using the marginal probabilities of each source). Using FA or PCA, we can find uncorrelated factors, but there's no way to assess whether they're also independent (normally, they aren't). In this section, we're going to study a model that's able to produce sparse representations (when the dictionary isn't under-complete) with a set of statistically independent components.

Let's assume we have a zero-centered and whitened dataset X sampled from N(0, I) and noiseless linear transformation:

In this case, the prior over is...