Conceptual Overview of Principal Components Analysis (PCA)
we seek to identify the hidden structure of the data set; or put another way, we are looking for the underlying dimensions (principal components) of the data set these dimensions are found by analyzing the interrelationships among the m variables (R-mode PCA) more specifically, the objective is to find the minimum number of causal influences or underlying dimensions (i.e., components in PCA and factors in FA) necessary to account for most of the variation within a data set – this is done by reducing the original dimensionality (m) of the data set to its true dimensionality (