Application of Principal Component Analysis to Elucidate Experimental and Theoretical Information

Abstract

Principal Component Analysis has been widely used in different scientific areas and for different purposes. The versatility and potentialities of this unsupervised method for data analysis, allowed the scientific community to explore its applications in different fields. Even when the principles of PCA are the same in what algorithms and fundamentals concerns, the strategies employed to elucidate information from a specific data set (experimental and/or theoretical), mainly depend on the expertise and needs of each researcher.