PRINCIPAL COMPONENT ANALYSIS & FACTOR ANALYSIS AS AN ENHANCED TOOL OF PATTERN RECOGNITION

It has been observed that authors have the confusion in principal component analysis and factor analysis. In this paper we are highlighting the differences as well as similarity in both analyses. These are two that can help reveal simpler patterns within a complex set of variables. Simpler Patterns means we can define complex set of variables in terms of much smaller number of variables called factors. Main think about PCA is that it is highly used in many real applications and theoretical applications but poorly understood. Main reason is that many authors regards it is same as factor analysis but it’s not like that. Factor analysis is a collection of methods used to examine how underlying constructs influence the responses on a number of measured variables