Human Motion De-noising via Greedy Kernel Principal Component Analysis Filtering

@article{Tangkuampien2006HumanMD,
  title={Human Motion De-noising via Greedy Kernel Principal Component Analysis Filtering},
  author={Therdsak Tangkuampien and David Suter},
  journal={18th International Conference on Pattern Recognition (ICPR'06)},
  year={2006},
  volume={3},
  pages={457-460}
}
Kernel principal component analysis (KPCA) has been shown to be a powerful non-linear de-noising technique. A disadvantage of KPCA, however, is that the storage of the kernel matrix grows quadratically, and the evaluation cost grows linearly with the number of exemplars. The size of the training set composing of these exemplars is therefore vital in any real system incorporating KPCA. Given long human motion sequences, we show how the greedy KPCA algorithm can be applied to filter exemplar… CONTINUE READING
Highly Cited
This paper has 31 citations. REVIEW CITATIONS
16 Citations
8 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 16 extracted citations

Similar Papers

Loading similar papers…