Maximum Correntropy Criterion

The Maximum Correntropy Criterion (MCC) is a robust statistical method used to improve the accuracy and efficiency of various signal processing and machine learning tasks by minimizing the influence of outliers and non-Gaussian noise. Current research focuses on integrating MCC into diverse algorithms, including Kalman filters for sensor fusion and iterative closest point methods for point cloud registration, as well as within neural networks and reinforcement learning frameworks. This approach enhances the reliability of estimations and predictions in applications ranging from robotics and computer vision to drug discovery and multi-agent systems, offering significant advantages over traditional least-squares methods in challenging real-world scenarios.