Pareto Optimization of Parameter Selection Speeds Up and Improves Quality of Motion Computation: Applying Evolutionary Multi-objective Optimization to Randomized-Subspace Robust PCA

David Grob, Mehmet Vurkac, Agnieszka Miguel, Mirka Mandich, Rana Bayrakcsmith. Pareto Optimization of Parameter Selection Speeds Up and Improves Quality of Motion Computation: Applying Evolutionary Multi-objective Optimization to Randomized-Subspace Robust PCA. In Hanghang Tong, Zhenhui Jessie Li, Feida Zhu, Jeffrey Yu, editors, 2018 IEEE International Conference on Data Mining Workshops, ICDM Workshops, Singapore, Singapore, November 17-20, 2018. pages 919-928, IEEE, 2018. [doi]

@inproceedings{GrobVMMB18,
  title = {Pareto Optimization of Parameter Selection Speeds Up and Improves Quality of Motion Computation: Applying Evolutionary Multi-objective Optimization to Randomized-Subspace Robust PCA},
  author = {David Grob and Mehmet Vurkac and Agnieszka Miguel and Mirka Mandich and Rana Bayrakcsmith},
  year = {2018},
  doi = {10.1109/ICDMW.2018.00134},
  url = {https://doi.org/10.1109/ICDMW.2018.00134},
  researchr = {https://researchr.org/publication/GrobVMMB18},
  cites = {0},
  citedby = {0},
  pages = {919-928},
  booktitle = {2018 IEEE International Conference on Data Mining Workshops, ICDM Workshops, Singapore, Singapore, November 17-20, 2018},
  editor = {Hanghang Tong and Zhenhui Jessie Li and Feida Zhu and Jeffrey Yu},
  publisher = {IEEE},
  isbn = {978-1-5386-9288-2},
}