In this design pattern, we will consider one way of implementing the dimensionality reduction through the usage of Principal Component Analysis (PCA) and Singular value decomposition (SVD), which are versatile techniques that are widely used for exploratory data analysis, creating predictive models, and for dimensionality reduction.
Dimensions in a given data can be intuitively understood as a set of all attributes that are used to account for the observed properties of data. Reducing the dimensionality implies the transformation of a high dimensional data into a reduced dimension's set that is proportional to the intrinsic or latent dimensions of the data. These latent dimensions are the minimum number of attributes that are needed to describe the dataset. Thus, dimensionality reduction is a method to understand the hidden structure of data that is used to mitigate the curse of high dimensionality and other...