principal component analysis (PCA)

principal component analysis (PCA) is an unsupervised chemometric method that simultaneously analyzes all multidimensional information. this is a way to condense the data set, increase interpretability and preserve statistical information as much as decrease data loss. For low dimensionality data, PCA provides an easy manner to describe the variation in a sample set, for instance, how the samples resemble each other. The high-dimensional data is represented in a new lower-dimensional subspace that's spanned via the principal components of the largest variance within the original variables. PCA has been carried out in lots of disciplines and has been identified as a beneficial tool for interpreting data generated through color sensors for evaluation and analysis.