论文标题
歧管自适应多个内核K均值用于聚类
Manifold Adaptive Multiple Kernel K-Means for Clustering
论文作者
论文摘要
基于K均值的多种内核方法旨在集成一组内核，以提高内核K-均值聚类的性能。但是，我们观察到，大多数现有的多个内核K-均值方法利用核内的非线性关系，而多个内核空间之间的局部歧管结构则没有得到充分考虑。在本文中，我们采用了多种自适应核，而不是原始核，以整合核的局部歧管结构。因此，诱导的多种流形自适应核不仅反映了非线性关系，还反映了局部流形结构。然后，我们在多个内核K-均值聚类框架中执行多个内核聚类。已验证的是，所提出的方法在各种数据集上优于几种最先进的基线方法。
Multiple kernel methods based on k-means aims to integrate a group of kernels to improve the performance of kernel k-means clustering. However, we observe that most existing multiple kernel k-means methods exploit the nonlinear relationship within kernels, whereas the local manifold structure among multiple kernel space is not sufficiently considered. In this paper, we adopt the manifold adaptive kernel, instead of the original kernel, to integrate the local manifold structure of kernels. Thus, the induced multiple manifold adaptive kernels not only reflect the nonlinear relationship but also the local manifold structure. We then perform multiple kernel clustering within the multiple kernel k-means clustering framework. It has been verified that the proposed method outperforms several state-of-the-art baseline methods on a variety of data sets.
