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

Factor Analysis

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

If p_data 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 p_lower such that the Kullback-Leibler divergence with p_data 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...