Principal component analysis (PCA) is a statistical technique for dimension reduction often used in clustering or factor analysis. Given any number of explanatory or causal variables, PCA ranks the variables by their ability to explain greatest variation in the data. It is this property that allows PCA to be used for dimension reduction, i.e. to identify the most important variables from amongst a large set possible influences.
Overview
Principal component analysis (PCA) is a statistical technique for dimension reduction often used in clustering or factor analysis. Given any number of explanatory or causal variables, PCA ranks the variables by their ability to explain greatest variation in the data. It is this property that allows PCA to be used for dimension reduction, i.e. to identify the most important variables from amongst a large set possible influences.
Mathematically, principal component analysis (PCA) amounts to an orthogonal transformation of possibly correlated variables (vectors) into uncorrelated variables called principal component vectors.
Tag usage
Questions on tag pca should be about implementation and programming problems, not about the statistical or theoretical properties of the technique. Consider whether your question might be better suited to Cross Validated, the StackExchange site for statistics, machine learning and data analysis.
In scientific software r for statistical computing and graphics, functions princomp and prcomp compute PCA.
