pca

Principal component analysis. PCA is an orthogonal linear transformation that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. PCA is theoretically the optimum transform for given data in least square terms. PCA can be thought of as revealing the internal structure of the data in a way which best explains the variance in the data. If a multivariate dataset is visualized as a set of coordinates in a high-dimensional data space, PCA supplies the user with a lower-dimensional picture when viewed from its (in some sense) most informative viewpoint.

PCA is mostly used as a tool in exploratory data analysis and for making predictive models. PCA involves the calculation of the eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centering the data for each attribute. The results of a PCA are usually discussed in terms of component scores and loadings.

As a linear technique, PCA is built for several purposes: first, it enables us to decorrelate the original variables; second, to carry out data compression, where we pay decreasing attention to the numerical accuracy by which we encode the sequence of principal components; third, to reconstruct the original input data using a reduced number of variables according to a least-squares criterion; and fourth, to identify potential clusters in the data.

In certain applications, PCA can be misleading. PCA is heavily influenced when there are outliers in the data. In other situations, the linearity of PCA may be an obstacle to successful data reduction and compression.

Parameters