07. Video: Dimensionality Reduction

Principal Components

A few takeaways from this video:

1. An advantage of Feature Extraction over Feature Selection is that the latent features can be constructed to incorporate data from multiple features, and thus retain more information present in the various original inputs, than just losing that information by dropping many original inputs.

2. Principal components are linear combinations of the original features in a dataset that aim to retain the most information in the original data.

3. You can think of a principal component in the same way that you think about a latent feature.

The general approach to this problem of high-dimensional datasets is to search for a projection of the data onto a smaller number of features which preserves the information as much as possible.

We'll take a closer look in the rest of this lesson.