Chapter 6

Dimensionality Reduction

When looking at data and plotting results, we can never go beyond three dimensions in our data, and usually find two dimensions easier to interpret. In addition, we have already seen the curse of dimensionality (Section 2.1.2) means that the higher the number of dimensions we have, the more training data we need. Further, the dimensionality is an explicit factor for the computational cost of many algorithms. These are some of the reasons why dimensionality reduction is useful. However, it can also remove noise, significantly improve the results of the learning algorithm, make the dataset easier to work with, and make the results easier to understand. In extreme cases such as the Self-Organising Map that we will ...

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