Also known as: PCA
Principal component analysis is a statistical technique that transforms correlated variables into a smaller set of uncorrelated variables called principal components. It is commonly used for dimensionality reduction while preserving as much variance in the data as possible.
A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component, in turn, has the highest variance possible under the constraint that it is orthogonal to the preceding com