- Eigenvalue Decomposition: Perform eigenvalue decomposition on the covariance or correlation matrix. This decomposition separates the matrix into eigenvalues and eigenvectors.
- Use of Eigenvectors: The eigenvectors represent directions of maximum variance and are orthogonal, meaning they can serve as a new basis for your parameters that are uncorrelated.
- Transformation: Transform your original correlated parameters using the eigenvectors. This step essentially rotates your parameter space to align with the directions of maximum variance indicated by the eigenvectors.
- Eigen Decomposition: "eigVec, eigVal = eig(C);" decomposes the covariance or correlation matrix C into its eigenvectors ("eigVec") and eigenvalues ("eigVal").
- Decorrelation: "decorrelatedParams = eigVec' * params;" transforms the parameters using the transpose of the eigenvectors, resulting in a new set of parameters that are decorrelated.
- For eigenvalue decomposition, refer to the documentation of "eig:" https://www.mathworks.com/help/matlab/ref/eig.html.
- For understanding covariance and correlation matrices, see: https://www.mathworks.com/help/matlab/ref/cov.html for covariance and https://www.mathworks.com/help/matlab/ref/corrcoef.html for correlation.