Principal component analysis (PCA) is a mathematical procedure that uses an Orthogonal matrix|orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of Correlation and dependence|linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. 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 be orthogonal to (i.e., uncorrelated with) the preceding components. Principal components are guaranteed to be independent only if the data set is multivariate normal distribution#Joint_normality|jointly normally distributed. PCA is sensitive to the relative scaling of the original variables. Depending on the field of application, it is also named the discrete Karhunen–Loève theorem|Karhunen–Loève transform (KLT), the Harold Hotelling|Hotelling transform or proper orthogonal decomposition (POD).
PCA was invented in 1901 by Karl Pearson. Now it is mostly used as a tool in exploratory data analysis and for making predictive modeling|predictive models. PCA can be done by Eigendecomposition of a matrix|eigenvalue decomposition of a data covariance (or correlation) matrix or singular value decomposition of a Data matrix (multivariate statistics)|data matrix, usually after mean centering (and normalizing or using Z-scores) the data matrix for each attribute. The results of a PCA are usually discussed in terms of component scores, sometimes called factor scores (the transformed variable values corresponding to a particular data point), and loadings (the weight by which each standardized original variable should be multiplied to get the component score).
PCA is the simplest of the true Eigenvectors|eigenvector-based multivariate analyses. Often, its operation can be thought of as revealing the internal structure of the data in a way that best explains the variance in the data. If a multivariate dataset is visualised as a set of coordinates in a high-Dimension (metadata)|dimensional data space (1 axis per variable), PCA can supply the user with a lower-dimensional picture, a shadow of this object when viewed from its (in some sense) most informative viewpoint. This is done by using only the first few principal components so that the dimensionality of the transformed data is reduced.PCA is closely related to factor analysis. Factor analysis typically incorporates more domain specific assumptions about the underlying structure and solves eigenvectors of a slightly different matrix.

Principal component analysis (PCA) is a mathematical procedure that uses an Orthogonal matrix|orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of Correlation and dependence|linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. 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 be orthogonal to (i.e., uncorrelated with) the preceding components. Principal components are guaranteed to be independent only if the data set is multivariate normal distribution#Joint_normality|jointly normally distributed. PCA is sensitive to the relative scaling of the original variables. Depending on the field...