Generalized Laplacian Eigenmaps for Modeling and Tracking Human Motions

@article{Rincn2014GeneralizedLE,
  title={Generalized Laplacian Eigenmaps for Modeling and Tracking Human Motions},
  author={J. M. D. Rinc{\'o}n and Michal Lewandowski and Jean-Christophe Nebel and D. Makris},
  journal={IEEE Transactions on Cybernetics},
  year={2014},
  volume={44},
  pages={1646-1660}
}
This paper presents generalized Laplacian eigenmaps, a novel dimensionality reduction approach designed to address stylistic variations in time series. It generates compact and coherent continuous spaces whose geometry is data-driven. This paper also introduces graph-based particle filter, a novel methodology conceived for efficient tracking in low dimensional space derived from a spectral dimensionality reduction method. Its strengths are a propagation scheme, which facilitates the prediction… Expand
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