Explore MATLAB source code curated for "SVD算法" with clean implementations, documentation, and examples.
The PCA algorithm used in pattern classification, including its Singular Value Decomposition (SVD) implementation, is primarily employed for dimensionality reduction and principal component extraction. This algorithm involves covariance matrix computation and eigenvalue decomposition techniques to identify the most significant features in high-dimensional data.
MATLAB GUI implementation for digital watermarking using DWT and SVD algorithms with chaotic encryption. This graduation project code combines discrete wavelet transform (DWT) and singular value decomposition (SVD) for watermark embedding and extraction. Features chaotic encryption for enhanced security. After downloading, ensure proper image path configuration in the code.
MATLAB implementation of Singular Value Decomposition (SVD) algorithm for compressing bitmap images in .bmp format, including detailed code explanation and compression methodology
While conventional MUSIC algorithm fails in DOA spectrum estimation under multipath environments, the SVD algorithm successfully estimates coherent signals by reconstructing the singular value matrix using eigenvectors corresponding to the largest eigenvalues.
MATLAB digital watermarking implementation using Discrete Wavelet Transform (DWT) method with four sub-band decomposition, focusing on LL sub-band selection. Combines Singular Value Decomposition (SVD) algorithm with DWT and employs chaotic encryption for security. Includes both GUI version (dwt_svd_GUI.rar) and M-file version for flexible implementation.