Partial least squares regression

Partial Least Squares Regression (PLSR), also known as Partial Least Squares Structural Equation Modeling (PLS-SEM), is a statistical method that bears similarities 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. This technique is particularly useful in scenarios where the predictors are many and highly collinear, or when the number of observations is less than the number of predictors, thereby making traditional regression models unsuitable.

Overview

PLSR is a form of regression that combines features from both principal component analysis (PCA) and multiple regression. It is particularly useful in analyzing data with complex, multidimensional relationships. PLSR projects the independent variables (X) and the dependent variables (Y) into a new space formed by latent variables, which are linear combinations of the original variables. The goal is to maximize the covariance between the projected X and Y.

Applications

PLSR has been widely applied in various fields such as chemometrics, sensory analysis, and social science research. In chemometrics, for example, it is used to predict the concentration of chemical constituents in a mixture based on spectroscopic data. In sensory analysis, PLSR can help in understanding how different sensory attributes contribute to the overall acceptability of a product. In social sciences, it is used to model complex relationships between observed variables and latent constructs.

Mathematical Formulation

The mathematical foundation of PLSR involves the decomposition of the original data matrices, X (predictors) and Y (responses), into a set of scores and loadings for both X and Y, such that the covariance between the X scores and Y scores is maximized. This is achieved through an iterative algorithm, typically starting with a simple linear regression of Y on X, followed by extraction of latent variables that explain the maximum variance-covariance relationship between X and Y.

Advantages and Limitations

One of the main advantages of PLSR is its ability to handle highly collinear, high-dimensional data sets where traditional regression fails. It is also less sensitive to outliers compared to other regression techniques. However, PLSR can be criticized for its lack of a statistical foundation in terms of inference, making it difficult to assess the significance of the model parameters. Moreover, the choice of the number of latent variables to extract is somewhat subjective and can affect the model's performance.

Software Implementation

Several statistical software packages offer PLSR functionality, including R (with packages like pls and mixOmics), MATLAB, and Python (through libraries such as scikit-learn). These tools provide comprehensive functions for performing PLSR analysis, including model fitting, cross-validation, and prediction.

Conclusion

Partial Least Squares Regression is a versatile and powerful statistical tool for modeling complex relationships between multivariate datasets. Despite its limitations, it remains a popular choice in many fields for its ability to uncover latent structures in data and predict outcomes with high-dimensional predictors.

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  Core Idea of Partial Least Squares
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  Deflation: The geometric interpretation of the deflation step in the PLS Algorithm