Chapter 5. Compressing Data via Dimensionality Reduction
In Chapter 4, Building Good Training Sets – Data Preprocessing, you learned about the different approaches for reducing the dimensionality of a dataset using different feature selection techniques. An alternative approach to feature selection for dimensionality reduction is feature extraction. In this chapter, you will learn about three fundamental techniques that will help us to summarize the information content of a dataset by transforming it onto a new feature subspace of lower dimensionality than the original one. Data compression is an important topic in machine learning, and it helps us to store and analyze the increasing amounts of data that are produced and collected in the modern age of technology.
In this chapter, we will cover the following topics:
- Principal Component Analysis (PCA) for unsupervised data compression
- Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing...
