AI & Machine Learning · Intermediate

Dimensionality Reduction

Techniques that reduce the number of input features while preserving as much useful information as possible.

Definition

Dimensionality reduction combats the curse of dimensionality, speeds training, reduces storage and enables visualization. Feature-extraction methods such as PCA create new combined features, while feature selection keeps a subset of the originals. Embeddings can be seen as learned low-dimensional representations.

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