@inproceedings{kim2015local,
  title={Local high-order regularization on data manifolds.},
  author={Kim, Kwang In and Tompkin, James and Pfister, Hanspeter and Theobalt, Christian},
  booktitle={\cvpr},
  pages={5473--5481},
  year={2015},
  doi={10.1109/CVPR.2015.7299186},
  url={http://ieeexplore.ieee.org/document/7299186/}
  abstract={The common graph Laplacian regularizer is well-established in semi-supervised learning and spectral dimensionality reduction. However, as a first-order regularizer, it can lead to degenerate functions in high-dimensional manifolds. The iterated graph Laplacian enables high-order regularization, but it has a high computational complexity and so cannot be applied to large problems. We introduce a new regularizer which is globally high order and so does not suffer from the degeneracy of the graph Laplacian regularizer, but is also sparse for efficient computation in semi-supervised learning applications. We reduce computational complexity by building a local first-order approximation of the manifold as a surrogate geometry, and construct our high-order regularizer based on local derivative evaluations therein. Experiments on human body shape and pose analysis demonstrate the effectiveness and efficiency of our method.}
}