In this recipe, we will work with robust principal components. Principal components are used to project data into a smaller subspace that is easier to work with. It is probably the most important dimensionality reduction technique.
Analyzing and working with lots of features is usually complicated for two main reasons:
- It's difficult to find patterns between them, because combinations of them might be relatively correlated.
- Modeling variables to predict another variable sometimes carries a significant amount of noise. Ideally, we would like to compress some of the information contained in the data in order to have a simpler model.
In order to introduce principal components, let's review a basic example. Let's assume we have a football and rugby score for some students:
|
Person |
Football |
Rugby |
|
John |
10 |
8 |
|
Michael |
3... |
