MATLAB Code Implementation for PLS Toolbox

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Resource Overview

An excellent MATLAB-based PLS toolbox capable of performing principal component analysis for multilinear data and partial least squares regression, featuring customizable parameters and validation methods.

Detailed Documentation

This documentation introduces a highly practical MATLAB-based PLS toolbox designed for multivariate data analysis, including principal component analysis and partial least squares regression. The toolbox incorporates additional functionalities such as cross-validation and model optimization, along with customizable parameter settings. Key implementation features include: algorithmic handling of multilinear data decomposition through iterative NIPALS or SIMPLS methods, covariance maximization between predictor and response variables, and residual analysis for model diagnostics. These capabilities enhance experimental accuracy and reliability while streamlining data analysis workflows through functions like plsregress() for core computations and custom cross-validation loops for performance assessment.

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