MATLAB Implementation of Partial Least Squares Algorithm
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Resource Overview
Highly practical MATLAB-coded Partial Least Squares method ready for immediate use in MATLAB environment, featuring comprehensive PLS algorithm explanation and implementation details
Detailed Documentation
This article presents a highly practical implementation of Partial Least Squares (PLS) method developed in MATLAB. The code is designed for seamless integration within MATLAB, allowing users to directly apply PLS algorithms for data analysis and modeling tasks. The implementation includes key functions for covariance matrix computation, dimension reduction, and regression coefficient calculation using the NIPALS algorithm. The package also provides detailed explanations of PLS methodology, covering fundamental concepts like latent variable extraction, cross-validation techniques, and variable importance projection. This resource serves as a comprehensive tool that combines ready-to-use MATLAB functions with theoretical background, enabling researchers to effectively perform multivariate data analysis and build robust predictive models.
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