  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.
