Dimensionality reduction is the process of converting a set of data with many variables into data with lesser dimensions while ensuring similar information. The aim is to reduce the number of dimensions in a dataset through either feature selection or feature extraction without significant loss of details. Feature selection approaches try to find a subset of the original variables. Feature extraction reduces the dimensionality of the data by transforming it into new features.
Principal Component Analysis (PCA) generates a new set of variables, among them uncorrelated, called principal components; each main component is a linear combination of the original variables. All principal components are orthogonal to each other, so there is no redundant information. The principal components as a whole, constitute an orthogonal basis for the data space. The goal of PCA is to explain the maximum amount of variance with the fewest number of principal...
