  In this paper, we study the robust subspace clustering problem, which aims to
cluster the given possibly noisy data points into their underlying subspaces. A
large pool of previous subspace clustering methods focus on the graph
construction by different regularization of the representation coefficient. We
instead focus on the robustness of the model to non-Gaussian noises. We propose
a new robust clustering method by using the correntropy induced metric, which
is robust for handling the non-Gaussian and impulsive noises. Also we further
extend the method for handling the data with outlier rows/features. The
multiplicative form of half-quadratic optimization is used to optimize the
non-convex correntropy objective function of the proposed models. Extensive
experiments on face datasets well demonstrate that the proposed methods are
more robust to corruptions and occlusions.
