Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS family of methods are known as bilinear factor models. Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical. PLS is used to find the fundamental relations between two matrices (X and Y), i.e. a latent variable approach to modeling the covariance structures in these two spaces. A PLS model will try to find the multidimensional direction in the X space that explains the maximum multidimensional variance direction in the Y space. PLS regression is particularly suited when the matrix of predictors has more variables than observations, and when there is multicollinearity among X values. By contrast, standard regression will fail in these cases (unless it is regularized). Partial least squares was introduced by the Swedish statistician Herman O. A. Wold, who then developed it with his son, Svante Wold. An alternative term for PLS is projection to latent structures, but the term partial least squares is still dominant in many areas. Although the original applications were in the social sciences, PLS regression is today most widely used in chemometrics and related areas. It is also used in bioinformatics, sensometrics, neuroscience, and anthropology. Underlying model The general underlying model of multivariate PLS is where is an matrix of predictors, is an matrix of responses; and are matrices that are, respectively, projections of (the X score, component or factor matrix) and projections of (the Y scores); and are, respectively, and orthogonal loading matrices; and matrices and are the error terms, assumed to be independent and identically distributed random normal variables. The decompositions of and are made so as to maximise the covariance between and . Algorithms A number of variants of PLS exist for estimating the factor and loading matrices and . Most of them construct estimates of the linear regression between and as . Some PLS algorithms are only appropriate for the case where is a column vector, while others deal with the general case of a matrix . Algorithms also differ on whether they estimate the factor matrix as an