  Principal component analysis (PCA) is often used for analyzing data in the
most diverse areas. In this work, we report an integrated approach to several
theoretical and practical aspects of PCA. We start by providing, in an
intuitive and accessible manner, the basic principles underlying PCA and its
applications. Next, we present a systematic, though no exclusive, survey of
some representative works illustrating the potential of PCA applications to a
wide range of areas. An experimental investigation of the ability of PCA for
variance explanation and dimensionality reduction is also developed, which
confirms the efficacy of PCA and also shows that standardizing or not the
original data can have important effects on the obtained results. Overall, we
believe the several covered issues can assist researchers from the most diverse
areas in using and interpreting PCA.
