*M.E. (C.S.E.) First Year, Sipna College of Engineering & Technology, Amravati
**Professor, Computer Science & Engineering Department Sipna College of Engineering & Technology, Amravati
Online published on 7 November, 2013.
Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) are both variable reduction techniques. There are distinct differences between PCA and EFA. Similarities and differences between PCA and EFA are studied in this paper. Principal Components retained account for a maximal amount of variance of observed variables while Factors account for common variance in the data. PCA decomposes correlation matrix while EFA decomposes adjusted correlation matrix. Exploring basic theory of multivariate analysis, which involves a mathematical procedure to transform a number of correlated variables into a number of uncorrelated variables have been studied, compared and analyzed for better performance.
Principal Component Analysis, Exploratory Factor Analysis, Principal Components, Correlated variables, uncorrelated variables, eigenvalues, eigenvectors
