Principal component analysis (PCA) is the most popular multivariate statistical technique for dimensionality reduction. It analyzes the training data consisting of several dependent variables, which are, in general, intercorrelated, and extracts important information from the training data in the form of a set of new orthogonal variables called principal components. We can perform PCA using two methods--eigen decomposition or singular value decomposition (SVD).
Principal component analysis
Getting ready
PCA reduces the n-dimensional input data to r-dimensional input data, where r<n. In simpler terms, PCA involves translating the origin and performing rotation of the axis such that one of the axes (the principal axis) has...
