Machine Learning with R, the tidyverse, and mlr

Chapter 15. Self-organizing maps and locally linear embedding

This chapter covers

• Creating self-organizing maps to reduce dimensionality
• Creating locally linear embeddings of high-dimensional data

In this chapter, we’re continuing with dimension reduction: the class of machine learning tasks focused on representing the information contained in a large number of variables, in a smaller number of variables. As you learned in chapters 13 and 14, there are multiple possible ways to reduce the dimensions of a dataset. Which dimension-reduction algorithm works best for you depends on the structure of your data and what you’re trying to achieve. Therefore, in this chapter, I’m going to add two more nonlinear dimension-reduction algorithms to your ...