Book Image

MATLAB for Machine Learning

By : Giuseppe Ciaburro
Book Image

MATLAB for Machine Learning

By: Giuseppe Ciaburro

Overview of this book

MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You’ll start by getting your system ready with t he MATLAB environment for machine learning and you’ll see how to easily interact with the Matlab workspace. We’ll then move on to data cleansing, mining and analyzing various data types in machine learning and you’ll see how to display data values on a plot. Next, you’ll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You’ll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you’ll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback
Improving the Performance of the Machine Learning Model - Dimensionality Reduction

Chapter 8. Improving the Performance of the Machine Learning Model - Dimensionality Reduction

When we handle large volumes of data, some issues occur spontaneously. How to build a representative model of a set of hundreds of variables? How to view data across countless dimensions? To address these issues, we must adopt a series of techniques called dimensionality reduction. Dimensionality reduction is the process of converting a set of data with many variables into data with lesser dimensions while ensuring similar information. The aim is to reduce the number of dimensions in a dataset through either feature selection or feature extraction without significant loss of details. Feature selection approaches try to find a subset of the original variables. Feature extraction reduces the dimensionality of the data by transforming it into new features.

Dimensionality reduction techniques are used to reduce two undesirable characteristics in data, namely noise (high variance values) and redundancy...