  In this paper, we present a kernel subspace clustering method that can handle
non-linear models. In contrast to recent kernel subspace clustering methods
which use predefined kernels, we propose to learn a low-rank kernel matrix,
with which mapped data in feature space are not only low-rank but also
self-expressive. In this manner, the low-dimensional subspace structures of the
(implicitly) mapped data are retained and manifested in the high-dimensional
feature space. We evaluate the proposed method extensively on both motion
segmentation and image clustering benchmarks, and obtain superior results,
outperforming the kernel subspace clustering method that uses standard
kernels[Patel 2014] and other state-of-the-art linear subspace clustering
methods.
