MATLAB Implementation of Canonical Correlation Analysis
MATLAB code implementation for canonical correlation analysis with correlation coefficient maximization
Canonical Correlation Analysis for Multivariate Data Analysis: Feature Dimensionality Reduction, Feature Fusion, and Correlation Analysis
MATLAB implementation of canonical correlation analysis for multivariate data processing including feature dimensionality reduction, feature fusion, and correlation analysis, featuring configurable parameters and modular code structure.
MATLAB Code Implementation for Canonical Correlation Analysis
A debugged canonical correlation analysis program ready for immediate execution, featuring robust data preprocessing and correlation coefficient computation.
Canonical Correlation Analysis Algorithm
This algorithm implements canonical correlation analysis, which is crucial for feature description and correlation analysis between multivariate datasets.
MATLAB Implementation of Canonical Correlation Analysis (CCA)
MATLAB code for performing Canonical Correlation Analysis between two images, with input parameters: input image 1, input image 2, rows, columns, channel 1, channel 2, and output matrix. The implementation involves preprocessing, feature extraction, and correlation computation using canonical correlation algorithms.
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.
Multivariate Data Analysis: Feature Dimensionality Reduction, Feature Fusion, and Correlation Analysis
Canonical Correlation Analysis code for multivariate data processing techniques including feature dimensionality reduction, feature fusion, and correlation analysis (commonly used mathematical modeling algorithms)
Principal Component Analysis and Canonical Correlation Analysis: Implementation and Case Studies
A practical guide to implementing PCA and CCA with real-world case studies, featuring code implementation approaches and result interpretation techniques.
Canonical Correlation Analysis for Fault Analysis Modeling
Implementing fault analysis modeling using canonical correlation analysis algorithm with Python code examples and algorithm insights
EEG SSVEP Signal Analysis
EEG SSVEP Signal Processing and Canonical Correlation Analysis