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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 in time and style, and a noise model coherent with the manifold, which prevents divergence, and increases robustness. Experiments show that a combination of both techniques achieves state-of-the-art performance for human pose tracking in underconstrained scenarios.
Martinez-del-Rincon, J., Lewandowski, M., Nebel, J-C., & Makris, D. (2014). Generalized Laplacian Eigenmaps for Modeling and Tracking Human Motions. IEEE Transactions on Cybernetics, 44(9), 1646-1660. https://doi.org/10.1109/TCYB.2013.2291497