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In many research fields, high-dimensional data poses significant difficulties for experimental algorithms and simulations. The pro-post of Principal Component Analysis (PCA) provides an important preprocessing idea for the study of large sample experiments. PCA achieves information refinement by constructing an uncorrelated variable. Based on basic and generalized ideas of PCA, we illustrate the feasibility and flexibility of its application in a wide range of involved fields. It demonstrates that PCA is a worthy method not only in different fields but also in specific applications as a classical theory that can be further investigated in depth according to the characteristics of the data.
Ruixin Yuan (Tue,) studied this question.