A Novel Blind Source Separation Method: Injecting Noise for ICA Component Stability Analysis

This paper introduces an innovative blind source separation technique that analyzes Independent Component Analysis (ICA) stability through controlled noise injection. The method effectively handles noisy environments and non-Gaussian signals while improving separation accuracy and robustness, with implementation insights including noise covariance optimization and stability metric calculations.

Blind Source Separation Method Based on Signal Second-Order Statistics

A Second-Order Blind Identification (SOBI) Approach for Blind Source Separation Using Second-Order Statistical Properties

KPCA: A Nonlinear Blind Source Separation Method with Kernel Function Implementation

KPCA serves as an effective nonlinear blind source separation technique, utilizing kernel methods for enhanced performance. Highly recommended for download and practical implementation in data analysis projects.