Feature Selection Combining PCA and ICA
Feature selection by combining PCA and ICA: performing principal component analysis first, followed by independent component analysis on the resulting features
Denoising Using Contourlet Transform, ICA, and Chaotic Particle Swarm Optimization
Image denoising through the integration of Contourlet transform, Independent Component Analysis (ICA), and chaotic particle swarm optimization algorithms for enhanced noise reduction and parameter optimization.
Fattal's Single Image Dehazing Algorithm Implementation
MATLAB implementation of Fattal's single image dehazing method utilizing Independent Component Analysis (ICA) with local patch-based assumption of uncorrelated surface shading and atmospheric transmission functions - effective for certain images but limited for others
Implementation of the Independent Component Analysis Algorithm Using JADE for Blind Source Separation
The JADE Algorithm for Independent Component Analysis: Separating Mixed Signals with Fourth-Order Cumulant-Based Joint Diagonalization
ICA: Independent Component Analysis
ICA: Independent Component Analysis implemented using MATLAB
Blind Source Separation – A Key Technology with ICA Implementation
Blind Source Separation (BSS) has emerged as a critical signal processing technology, with Independent Component Analysis (ICA) serving as its primary technique. ICA enables the separation of mixed non-Gaussian source signals into their original components without prior information about the mixing process. This MATLAB implementation demonstrates ICA-based Multiuser Detection (MUD) in a CDMA system, showcasing separation of 30 unique user signals at the Base Transceiver Station (BTS).
Fast Independent Component Analysis (FastICA) Source Code
FastICA source code implementation with demonstration routines for ICA applications in MRI image processing, featuring signal separation and denoising algorithms.
PCA (Principal Component Analysis) and ICA (Independent Component Analysis) with MATLAB Implementation
MATLAB source code implementations for PCA and ICA - two classical feature extraction methods widely used in image processing, featuring dimensionality reduction and signal separation capabilities
MATLAB Implementation of ICA and PCA for Process Monitoring and Fault Diagnosis
This algorithm implements Independent Component Analysis (ICA) and Principal Component Analysis (PCA) for process monitoring and fault diagnosis, including automated data structure adaptation for enhanced stability and reliability.
Major Algorithms for Feature Extraction: Independent Component Analysis (ICA)
This uploaded code implements one of the primary feature extraction algorithms: Independent Component Analysis (ICA), which demonstrates superior performance compared to Principal Component Analysis (PCA) in various applications.