Kernel method is an effective way to solve the problem of nonlinear mode analysis, and its key is the selection or construction of kernel function. This paper firstly induced entropy-like divergence by combining Jensen-Shannon/Bregman divergence with convex function, its mercer kernel function called entropy-like divergence kernel is also constructed. Secondly, an adaptive noise distance based on entropy-like divergence kernel and a novel fuzzy weighted local factor of robust fuzzy clustering are presented, and they are also embedded into the objective function of fuzzy C-means clustering with noise cluster. In the end, a novel noise-resistant fuzzy weighed local information clustering based on entropy-like divergence kernel (NEKWFLICM) is proposed, and its convergence is strictly proved by convergence theorem of alternating iteration. Many experimental results delicate that the proposed algorithm has more robust and accurate than a series of existing state-of-the-art Gaussian kernel-based fuzzy clustering-related segmentation algorithms in the presence of high noise.

核方法是解决非线性模式分析问题的有效方法，其关键是核函数的选择或构造。本文首先将Jensen-Shannon / Bregman发散与凸函数相结合来诱发类似熵的发散，并构造了它的Mercer核函数，即类似熵的发散核。其次，提出了一种基于熵的散度核的自适应噪声距离和鲁棒模糊聚类的新型模糊加权局部因子，并将其嵌入到带有噪声聚类的模糊C均值聚类的目标函数中。最后，提出了一种基于类熵散度核（NEKWFLICM）的新型抗噪模糊加权局部信息聚类，并通过交替迭代的收敛定理严格证明了其收敛性。