MATLAB Program for Incomplete Data Analysis
MATLAB program for incomplete data analysis (partial information reconstruction): Minimum Mean Square Estimation, Covariance Matrix Computation, Missing Value Imputation
MATLAB Code Implementation of PCA Steps
PCA Implementation Steps: 1. Center the data (mean normalization); 2. Compute the covariance matrix; 3. Calculate eigenvalues and eigenvectors of the covariance matrix; 4. Sort eigenvalues and corresponding eigenvectors; 5. Determine projection direction based on target dimensionality d'; 6. Compute dimensionally reduced data
PCA Algorithm Programming Design Steps
PCA Algorithm Programming Design Steps: 1. Mean Centering 2. Compute Covariance Matrix and its Eigenvalues/Eigenvectors 3. Count Eigenvalues Exceeding Threshold 4. Sort Eigenvalues in Descending Order 5. Remove Small Eigenvalues 6. Remove Large Eigenvalues (Typically Omitted) 7. Combine Selected Eigenvalues 8. Select Corresponding Eigenvalues/Eigenvectors 9. Compute Whitening Matrix 10. Extract Principal Components
Random Sampling of the Above Image Using a 9×9 Window
(1) Apply a 9×9 window to randomly sample the above image, extracting a total of 200 sub-images; (2) Convert each sub-image into an 81-dimensional row vector by concatenating all columns; (3) Perform KL transformation on all 200 row vectors, compute the eigenvectors and eigenvalues of the corresponding covariance matrix, and arrange eigenvalues and corresponding eigenvectors in descending order; (4) Select the eigenvectors corresponding to the top 40 largest eigenvalues as principal components, project the original image blocks onto these 40 eigenvectors, and use the obtained projection coefficients as the feature vector for each sub-block; (5) Compute the feature vectors for all sub-blocks.
Polarimetric SAR Image Processing: Covariance Matrix Conversion to Stokes/Mueller Matrices
Implementation of algorithms for converting covariance matrices to Stokes matrices or Mueller matrices in polarimetric SAR image processing, including key computational methods and transformation procedures for enhanced data visualization and analysis.
Pauli Decomposition Algorithm for Covariance Matrix Data in Polarimetric SAR Images
Implementation of Pauli decomposition for processing covariance matrix data in polarimetric SAR imagery, featuring efficient matrix operations and multi-channel image generation capabilities
MUSIC Algorithm for Signal and Noise Subspace Separation using Covariance Matrix
The MUSIC algorithm separates signal and noise subspaces by eigen-decomposition of the received data covariance matrix (Rx). It constructs spatial scanning spectra by exploiting the orthogonality between signal steering vectors and noise subspace, then performs peak searching in the parameter domain for accurate signal parameter estimation. Implementation typically involves eigenvalue decomposition, subspace identification, and peak detection algorithms.
Simulation of Sparse Representation DOA Estimation Algorithm for Broadband Signal Covariance Matrix
Simulation of DOA Estimation Algorithm Using Sparse Representation of Broadband Signal Covariance Matrix
Sparse DOA Estimation via Covariance Matrix with Implementation Insights
Sparse DOA using covariance matrix methodology, providing valuable reference for compressed sensing-based localization with algorithm implementation details
MATLAB Code Implementation for Principal Component Analysis Function
MATLAB code implementation of Principal Component Analysis (PCA) function with detailed algorithmic explanations