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

Biclustering

Biclustering is a family of methods that operate on matrices whose rows and columns represent different features connected with a precise rationale. For example, the rows can represent customers, and the columns products. Each element can indicate a rating or, if zero, the fact that a specific product, p_j, has not been bought/rated by the customer, c_i. As the behavior of the customers can generally be segmented into specific sets, we can assume that A has an underlying checkerboard structure, where the compact regions, called biclusters, represent sub-matrices with peculiar properties.

The nature of such properties depends on the specific context, but the structures share the common feature of being strongly separated from the remaining regions. In our example, the biclusters can be mixed segments containing sets of customers and products that agree on the rating (this concept will be clearer in the practical example), but more generally, the rearrangement of rows...