Genetic Algorithm for Optimal Band Selection
Combining Genetic Algorithm with Partial Least Squares Regression for spectral band optimization
Genetic Algorithm-Partial Least Squares (GA-PLS) for Quantitative Analysis
Genetic Algorithm-Partial Least Squares method for quantitative analysis, combining evolutionary optimization with multivariate statistical modeling for enhanced data processing efficiency.
MATLAB Implementation of Partial Least Squares Algorithm
Highly practical MATLAB-coded Partial Least Squares method ready for immediate use in MATLAB environment, featuring comprehensive PLS algorithm explanation and implementation details
Partial Least Squares (PLS) Regression with NIPALS Algorithm Implementation
Partial Least Squares (PLS) regression is widely applied across numerous domains. This package provides a comprehensive function implementing PLS regression using the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm, accompanied by detailed tutorial materials explaining the algorithm's mechanics and practical implementation.
Modeling Code for Partial Least Squares (PLS), Interval Partial Least Squares (siPLS), and Synergy Interval Partial Least Squares Methods
Implementation code for various partial least squares modeling techniques including PLS, siPLS, and synergy interval PLS, with enhanced algorithm explanations and implementation details.
Regression Analysis using Combined Genetic Algorithm and Partial Least Squares
This implementation demonstrates an excellent program combining Genetic Algorithm and Partial Least Squares for regression, featuring optimized variable selection and enhanced predictive accuracy.
A Simple Algorithm for Partial Least Squares Regression
This article presents straightforward implementation approaches for Partial Least Squares (PLS) algorithm, providing programming references and code examples for collaborative development and knowledge sharing.
PLS iToolbox
Application Background For a long time, there has been a clear distinction between model-based methods and epistemological approaches. Partial Least Squares (PLS) organically integrates these two methodologies, enabling simultaneous implementation of regression modeling (multivariate linear regression), data structure simplification (principal component analysis), and correlation analysis between two variable sets (canonical correlation analysis) within a single algorithm. This represents a significant breakthrough in multivariate statistical data analysis. Key Technology As a multivariate linear regression method, the primary objective of PLS regression is to establish a linear model: Y=XB+E, where Y is the response matrix with m variables and n sample points, X is the predictor matrix with p variables and n sample points, B is the regression coefficient matrix, and E represents the noise correction model with the same dimensions as Y. Typically, variables X and Y are standardized before computation by subtracting their means and dividing by standard deviations.
Partial Least Squares Method
The Partial Least Squares (PLS) method refers to performing principal component analysis for dimensionality reduction on datasets before conducting linear regression analysis based on least squares. The following source code is provided in its complete form by the GreenSim team for free use, with proper attribution required to GreenSim team (http://blog.sina.com.cn/greensim). The implementation includes key components for covariance maximization and projection calculations.
Partial Least Squares Toolbox
Eigenvector's Partial Least Squares Toolbox - Version 3.0. Standalone/no installation required. Includes PLS regression, discriminant analysis, and multivariate calibration algorithms with MATLAB-compatible functions.