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Locally weighted PCA regression to recover missing markers in human motion data

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  • Hai Dang Kieu
  • Hongchuan Yu
  • Zhuorong Li
  • Jian Jun Zhang

Abstract

“Missing markers problem”, that is, missing markers during a motion capture session, has been raised for many years in Motion Capture field. We propose the locally weighted principal component analysis (PCA) regression method to deal with this challenge. The main merit is to introduce the sparsity of observation datasets through the multivariate tapering approach into traditional least square methods and develop it into a new kind of least square methods with the sparsity constraints. To the best of our knowledge, it is the first least square method with the sparsity constraints. Our experiments show that the proposed regression method can reach high estimation accuracy and has a good numerical stability.

Suggested Citation

  • Hai Dang Kieu & Hongchuan Yu & Zhuorong Li & Jian Jun Zhang, 2022. "Locally weighted PCA regression to recover missing markers in human motion data," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-13, August.
    • Handle: RePEc:plo:pone00:0272407
    DOI: 10.1371/journal.pone.0272407
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    References listed on IDEAS

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    1. Mickaël Tits & Joëlle Tilmanne & Thierry Dutoit, 2018. "Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-21, July.
    2. Furrer, Reinhard & Bachoc, François & Du, Juan, 2016. "Asymptotic properties of multivariate tapering for estimation and prediction," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 177-191.
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