Manifold learning

Manifold learning is like figuring out how to make a map of the world, but without having to use any directions or coordinates. Imagine you have a giant pile of puzzle pieces representing all the different places in the world. There are too many to put together to create a regular map, so instead you have to figure out how the pieces fit together in a way that makes sense.

To do this, you start by looking for similarities between the puzzle pieces. You notice that some pieces have oceans on them, while others have mountains, and some have cities. You group the pieces together based on these similarities, and soon you realize that the puzzle pieces can be organized into different regions based on their features.

This is similar to what happens with manifold learning. Instead of puzzle pieces, we have data points that represent different things, like people or objects. Similarly, these data points have different features that can be used to group them together. By identifying these similarities and grouping the data points together, we can create a sort of map that shows how the different data points are related to each other.

In this way, manifold learning helps us to understand complex datasets and find patterns that might not be immediately obvious. It's like building a map of the world, but for data!