An Introduction to Principal Component Analysis and Its Applications

Abstract

Huge datasets are progressively normal and are frequently hard to decipher. Principle component analysis (PCA) is used for reducing dimensions of huge datasets and thus used in expanding interpretability by minimizing information loss. This is achieved by making uncorrelated variables that successively maximize variance. PCA finds eigen values/eigen vectors, and the new factors are characterized by the current dataset, and thus making PCA a versatile data analysis technique. This chapter will focus on explaining the working of PCA with various mathematical proofs and derivations along with discussion on the advantages and disadvantages of it. This chapter lucidly explains the working of PCA in detail along with the various mathematical proofs and derivations.