The full information on the theory of principal component analysis may be found here. This article is about practice in R. It covers main steps in data preprocessing, compares R results with theoretical calculations, shows how to analyze principal components and use it for dimensionality reduction. The last section is devoted to modelling using principal components and comparing it to LDA.
Tag: dimesionality reduction
Principal Component Analysis
Principal component analysis (PCA) is an unsupervised method of generating components from a large set of variables available if a data set which represent a combinations of features that capture as much information in the data as possible. Or in a Wikipedia way:
A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
Data Science Diving 