  We present a robust multiple manifolds structure learning (RMMSL) scheme to
robustly estimate data structures under the multiple low intrinsic dimensional
manifolds assumption. In the local learning stage, RMMSL efficiently estimates
local tangent space by weighted low-rank matrix factorization. In the global
learning stage, we propose a robust manifold clustering method based on local
structure learning results. The proposed clustering method is designed to get
the flattest manifolds clusters by introducing a novel curved-level similarity
function. Our approach is evaluated and compared to state-of-the-art methods on
synthetic data, handwritten digit images, human motion capture data and
motorbike videos. We demonstrate the effectiveness of the proposed approach,
which yields higher clustering accuracy, and produces promising results for
challenging tasks of human motion segmentation and motion flow learning from
videos.
