Chapter 10. Dimension Reduction

In this chapter, we will cover the following topics:

  • Performing feature selection with FSelector
  • Performing dimension reduction with PCA
  • Determining the number of principal components using a scree test
  • Determining the number of principal components using the Kaiser method
  • Visualizing multivariate data using biplot
  • Performing dimension reduction with MDS
  • Reducing dimensions with SVD
  • Compressing images with SVD
  • Performing nonlinear dimension reduction with ISOMAP
  • Performing nonlinear dimension deduction with Local Linear Embedding

Introduction

Most datasets contain features (such as attributes or variables) that are highly redundant. In order to remove irrelevant and redundant data to reduce the computational cost and avoid ...

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