Communication Reduction in distributed estimation using correntropy as an information criterion

Robust diffusion maximum correntropy criterion algorithm for distributed network estimation

Robust diffusion algorithms based on the maximum correntropy criterion(MCC) are developed to address the distributed networks estimation issue in impulsive(long-tailed) noise environments. The cost functions used in distributed network estimation are in general based on the mean square error (MSE) criterion, which is optimal only when the measurement noise is Gaussian. In non-Gaussian situation...

Pose Estimation in SAR using an Information Theoretic Criterion

This paper describes a pose estimation algorithm based on an information theoretic formulation. We formulate the pose estimation statistically and show that pose can be estimated from a low dimensional feature space obtained by maximizing the mutual information between the aspect angle and the output of the mapper. We use the Havrda-Charvat definition of entropy to implement a nonparametric est...

Neuro-Fuzzy Classification Using the Correntropy Criterion: Application to Sleep Depth Estimation

Mesh Reduction Using an Angle Criterion Approach

Surface polygonization is the process by which a representative polygonal mesh of a surface is constructed for rendering or analysis purposes. This work presents a new surface polygonization algorithm specifically tailored to be applied to a large class of models which are created with parametric surfaces having triangular meshes. This method has particular application in the area of building v...

Smooth Boosting Using an Information-Based Criterion

Smooth boosting algorithms are variants of boosting methods which handle only smooth distributions on the data. They are proved to be noise-tolerant and can be used in the “boosting by filtering” scheme, which is suitable for learning over huge data. However, current smooth boosting algorithms have rooms for improvements: Among non-smooth boosting algorithms, real AdaBoost or InfoBoost, can per...