Locally weighted PCA regression to recover missing markers in human motion data
Authors: Kieu, H.D., Yu, H., Li, Z. and Zhang, J.J.
Journal: PLoS ONE
Volume: 17
Issue: 8 August
eISSN: 1932-6203
DOI: 10.1371/journal.pone.0272407
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.
https://eprints.bournemouth.ac.uk/37350/
Source: Scopus
Locally weighted PCA regression to recover missing markers in human motion data.
Authors: Kieu, H.D., Yu, H., Li, Z. and Zhang, J.J.
Journal: PLoS One
Volume: 17
Issue: 8
Pages: e0272407
eISSN: 1932-6203
DOI: 10.1371/journal.pone.0272407
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.
https://eprints.bournemouth.ac.uk/37350/
Source: PubMed
Locally weighted PCA regression to recover missing markers in human motion data.
Authors: Kieu, H.D., Yu, H., Li, Z. and Zhang, J.J.
Journal: PloS one
Volume: 17
Issue: 8
Pages: e0272407
eISSN: 1932-6203
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0272407
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.
https://eprints.bournemouth.ac.uk/37350/
Source: Europe PubMed Central
Locally weighted PCA regression to recover missing markers in human motion data.
Authors: Kieu, H.D., Yu, H., Li, Z. and Zhang, J.J.
Journal: PLoS One
Volume: 17
Issue: 8
ISSN: 1932-6203
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.
https://eprints.bournemouth.ac.uk/37350/
Source: BURO EPrints