ABSTRACT
This chapter discusses the most commonly used dimensionality reduction technique, Principal Component Analysis (PCA), based on linear projection. This technique projects data to lower dimensional space by maximizing the variance in order to preserve the maximum possible information. The principal components are obtained by finding the solution to an eigenproblem, that is, by performing singular value decomposition on the data matrix. This chapter also covers the underlying theory of this algorithm, derives the mathematics and discusses its applications and some examples. Furthermore, its advantages and limitations and cases where PCA would be best suited are also covered.
