Complex quantized minimum error entropy with fiducial points: theory and application in model regression.

Minimum error entropy with fiducial points (MEEF) has gained significant attention due to its excellent performance in mitigating the adverse effects of non-Gaussian noise in the fields of machine learning and signal processing. However, the original MEEF algorithm suffers from high computational complexity due to the double summation of error samples. The quantized MEEF (QMEEF), proposed by Zheng et al.

On extending the complex FastICA algorithms to noisy data.

Independent component analysis (ICA) methods are widely applied to modern digital signal processing. The complex-valued FastICA algorithms are one type of the most significant methods. However, the complex ICA model usually omits the noise.