Principal components analysis (PCA) is a technique used to reduce multidimensional data sets to lower dimensions for analysis. Depending on the field of application, it is also named the discrete Karhunen-Loève transform, the Hotelling transform or proper orthogonal decomposition (POD).
Eigenvalue, eigenvector and eigenspace Singular value decomposition Diagonal matrix Kernel principal component analysis Nonlinear dimensionality reduction Outer product Covariance matrix Standard score R (programming language) Linear map Arithmetic mean MATLAB Correlation Auto-encoder Singular Spectrum Analysis Point Distribution Model Oja's rule Principal component regression Factorial code Dimension reduction Empirical orthogonal functions Scatter matrix Minimum mean square error Transform coding IDL (programming language) SciPy Eigenface Rayleigh quotient Arg max Karhunen-Loève theorem IDL Matrix algebra K-means algorithm Independent component analysis Exploratory data analysis Linear discriminant analysis Matrix decomposition Projection (mathematics) Deviation Harold Hotelling
