Results 151 to 160 of about 1,489 (178)
Some of the next articles are maybe not open access.
Robust Multidimensional Scaling Using a Maximum Correntropy Criterion
IEEE Transactions on Signal Processing, 2017Multidimensional scaling (MDS) refers to a class of dimensionality reduction techniques, which represent entities as points in a low-dimensional space so that the interpoint distances approximate the initial pairwise dissimilarities between entities as closely as possible. The traditional methods for solving MDS are susceptible to outliers.
Fotios D. Mandanas +1 more
openaire +1 more source
Kernel adaptive filtering under generalized Maximum Correntropy Criterion
2016 International Joint Conference on Neural Networks (IJCNN), 2016Owing to their universal approximation capability and online learning manner, kernel adaptive filters have been widely used in nonlinear systems modeling. Under Gaussian assumption, traditional kernel adaptive algorithms utilize the well-known mean square error(MSE) as a cost function to get optimal solutions.
Yicong He +4 more
openaire +1 more source
Robust multidimensional scaling using a maximum correntropy criterion
2014, ,
openaire +1 more source
Kernel Least Mean Square With Maximum Correntropy Criterion
2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS), 2022Yawen Li, Wenling Li, Zhe Xue, Ang Li
openaire +1 more source
Adaptive Convex Combination of Kernel Maximum Correntropy Criterion
2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP), 2022Long Shi, Yunchen Yang
openaire +1 more source
An Efficient Parameter Optimization of Maximum Correntropy Criterion
IEEE Signal Processing Letters, 2023Long Shi, Lu Shen, Badong Chen
openaire +1 more source
Maximum Correntropy Criterion–Based Kernel Adaptive Filters
2018Badong Chen, Xin Wang, Jose C. Principe
openaire +1 more source