fastICA Algorithm Implementation: Independent Component Analysis (ICA)
fastICA Algorithm Implementation for Independent Component Analysis (ICA, Independent Component Correlation Algorithm)
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
Implementation of Independent Component Analysis: A Collection of MATLAB Programs
This toolkit provides multiple MATLAB programs for implementing Independent Component Analysis, featuring algorithms like FastICA and JADE with practical code examples for signal processing applications.
Independent Component Analysis
Independent Component Analysis (ICA) is a powerful data analysis tool that has emerged in recent years. It was first mathematically defined by Comon in 1994, building upon concepts originally introduced by Herault and Jutten in 1986. Despite its relatively recent development, ICA has gained significant theoretical and practical attention globally, becoming a prominent research focus. Its implementation typically involves optimization algorithms like FastICA or InfoMax to separate statistically independent source signals from mixed observations. Applications span blind source separation, image processing, speech recognition, biomedical signal processing, and financial data analysis, making it an extension of Principal Component Analysis (PCA) with broader independence constraints.
Application of Fuzzy Support Vector Machines and Independent Component Analysis in Face Recognition
Implementation of Fuzzy SVM and ICA Algorithms for Enhanced Face Recognition Systems
Independent Component Analysis Algorithm
Algorithm for Independent Component Analysis with Implementation Approaches
Implementation of Fast Independent Component Analysis Using Fixed-Point Algorithm
Implementation of fast independent component analysis using fixed-point algorithm, with MATLAB code demonstrating signal decomposition and component extraction techniques
Independent Component Analysis (ICA) Algorithm
AMUSE - An Independent Component Analysis (ICA) Algorithm for Blind Separation of Mixed Speech Signals, implementing second-order statistics and time-delayed covariance matrices for source separation
MATLAB Implementation of FastICA Algorithm for Blind Source Separation
Complete MATLAB implementation of the FastICA algorithm with full source code. This package provides a comprehensive blind source separation tool using Independent Component Analysis (ICA), featuring detailed algorithm explanations and ready-to-use code examples for signal processing applications.
ICA Applications in Brain Activation Studies: Algorithm Implementation and fMRI Analysis
ICA achieves dimensionality reduction and isolates independent neural activations in brain imaging studies. This demonstration compares icaMS, icaML, icaMF, icaMF (positive sources), and PCA algorithms using two fMRI datasets: human scans from Hvidovre University Hospital (Denmark) and monkey scans from the EU-funded MAPAWAMO project.