Partial least squares regression
|Part of a series on Statistics|
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).
The PLS algorithm is employed in partial least squares path modeling, a method of modeling a "causal" network of latent variables (causes cannot be determined without experimental or quasi-experimental methods, but one typically bases a latent variable model on the prior theoretical assumption that latent variables cause manifestations in their measured indicators). This technique is argued to be a form of structural equation modeling, distinguished from the classical method by being component-based rather than covariance-based. Yet, others dispute that this is the case.
Partial least squares was introduced by the Swedish statistician Herman Wold, who then developed it with his son, Svante Wold. An alternative term for PLS (and more correct according to Svante Wold) 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. In contrast, PLS path modeling is most often used in social sciences, econometrics, marketing and strategic management. However, within the realm of psychology, it has received criticism for being an unreliable estimation and testing tool.
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 .
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 orthogonal, an orthonormal matrix or not. The final prediction will be the same for all these varieties of PLS, but the components will differ.
PLS1 is a widely used algorithm appropriate for the vector Y case. It estimates T as an orthonormal matrix. In pseudocode it is expressed below (capital letters are matrices, lower case letters are vectors if they are superscripted and scalars if they are subscripted):
1 function PLS1() 2 3 , an initial estimate of w. 4 5 for to l 6 (note this is a scalar) 7 8 9 (note this is a scalar) 10 if 11 , break the for loop 12 if 13 14 15 16 end for 17 define W to be the matrix with columns . Do the same to form the P matrix and q vector. 18 19 20 return
This form of the algorithm does not require centering of the input X and Y, as this is performed implicitly by the algorithm. This algorithm features 'deflation' of the matrix X (subtraction of ), but deflation of the vector y is not performed, as it is not necessary (it can be proved that deflating y yields the same results as not deflating.). The user-supplied variable l is the limit on the number of latent factors in the regression; if it equals the rank of the matrix X, the algorithm will yield the least squares regression estimates for B and
In 2002 a new method was published called orthogonal projections to latent structures (OPLS). In OPLS, continuous variable data is separated into predictive and uncorrelated information. This leads to improved diagnostics, as well as more easily interpreted visualization. However, these changes only improve the interpretability, not the predictivity, of the PLS models. L-PLS extends PLS regression to 3 connected data blocks. Similarly, OPLS-DA (Discriminant Analysis) may be applied when working with discrete variables, as in classification and biomarker studies.
In 2015 partial least squares was related to a procedure called the three-pass regression filter (3PRF). Supposing the number of observations and variables are large, the 3PRF (and hence PLS) is asymptotically normal for the "best" forecast implied by a linear latent factor model. In stock market data, PLS has been shown to provide accurate out-of-sample forecasts of returns and cash-flow growth.
- Feature extraction
- Data mining
- Machine learning
- Regression analysis
- Canonical correlation
- Deming regression
- Multilinear subspace learning
- Principal component analysis
- Total sum of squares
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- imDEV free Excel add-in for PLS and PLS-DA
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