Euclidean Direction Search Algorithm Based on Maximum Correntropy Criterion
Jie Wang, Lu Lu, Long Shi, Guangya Zhu, Xiaomin Yang
Abstract
The Euclidean direction search (EDS) algorithm can reduce the complexity by avoiding the matrix inversion operation. However, it may fail to work in impulsive environments. To address this problem, a novel EDS based upon the maximum correntropy criterion (EDS-MCC) algorithm is proposed, which provides computational savings and robustness for combating impulsive noise. Additionally, the EDS-MCC algorithm is analyzed to obtain the theoretical performance by utilizing the energy conservation argument (ECA) and the Taylor expansion method. Simulations are exhibited to show the robustness of the EDS-MCC algorithm and verify the accuracy of the theoretical analysis.
Topics & Concepts
Robustness (evolution)AlgorithmEuclidean distanceComputational complexity theoryComputer scienceMathematical optimizationInversion (geology)MathematicsArtificial intelligenceStructural basinPaleontologyGeneChemistryBiologyBiochemistryAdvanced Adaptive Filtering TechniquesBlind Source Separation TechniquesSpeech and Audio Processing