ABSTRACT
A complex system may involve large volumes of data with more than hundreds of different variables. In such situations, it is desirable to find ways to effectively reduce the dimensions of the data with minimum loss of information, and interpret the data with physical meanings. Principal component analysis (PCA) provides the capability to identify patterns in high-dimensional data and reconstruct it to highlight similarities and differences.
