Dimensionality reduction – the Principal Component Analysis design pattern

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.

Background

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 ...

Get Pig Design Patterns now with the O’Reilly learning platform.

O’Reilly members experience live online training, plus books, videos, and digital content from nearly 200 publishers.