Dimensionality reduction includes a set of techniques to help deal with the problem of the curse of dimensionality. These techniques are aimed at reducing the number of variables to be considered by the models we build, generally falling into feature selection and feature extraction.
In the context of machine learning, the term feature extraction is associated with techniques that seek to build a dataset derived and transformed from the original data.
One of the best known and most used techniques to reduce the dimensionality is Principal Components Analysis or PCA.
Principal component analysis (PCA) is a technique to reduce the dimensionality of a dataset, in fact, it is one of the most well known ...