Limitations of One Hot Encoding
The operation of one-hot encoding categorical variables is actually a simple embedding where each category is mapped to a different vector. This process takes discrete entities and maps each observation to a vector of 0s and a single 1 signaling the specific category.

The one-hot encoding technique has two main drawbacks:

For high-cardinality variables — those with many unique categories — the dimensionality of the transformed vector becomes unmanageable.
The mapping is completely uninformed: “similar” categories are not placed closer to each other in embedding space.
The first problem is well-understood: for each additional category — referred to as an entity — we have to add another number to the one-hot encoded vector. If we have 37,000 books on Wikipedia, then representing these requires a 37,000-dimensional vector for each book, which makes training any machine learning model on this representation infeasible.

The second problem is equally limiting: one-hot encoding does not place similar entities closer to one another in vector space. If we measure similarity between vectors using the cosine distance, then after one-hot encoding, the similarity is 0 for every comparison between entities.

This means that entities such as War and Peace and NAME_1 NAME_2 (both classic books by NAME_3) are no closer to one another than War and Peace is to The Hitchhiker’s Guide to the Galaxy if we use one-hot encoding.

Can I get the 5 main points from the text above? Put it in a list please.