ML Anthology

Robust Multilinear Principal Component Analysis

Kohei Inoue, Kenji Hara, Kiichi Urahama
ICCV 2009 pp. 591-597
doi:10.1109/ICCV.2009.5459186 /iccv/2009/inoue2009iccv-robust/

Abstract

We propose two methods for robustifying multilinear principal component analysis (MPCA) which is an extension of the conventional PCA for reducing the dimensions of vectors to higher-order tensors. For two kinds of outliers, i.e., sample outliers and intra-sample outliers, we derive iterative algorithms on the basis of the Lagrange multipliers. We also demonstrate that the proposed methods outperform the original MPCA when datasets contain such outliers experimentally.

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Cite

Text

Inoue et al. "Robust Multilinear Principal Component Analysis." IEEE/CVF International Conference on Computer Vision, 2009. doi:10.1109/ICCV.2009.5459186

Markdown

[Inoue et al. "Robust Multilinear Principal Component Analysis." IEEE/CVF International Conference on Computer Vision, 2009.](https://mlanthology.org/iccv/2009/inoue2009iccv-robust/) doi:10.1109/ICCV.2009.5459186

BibTeX

@inproceedings{inoue2009iccv-robust,
  title     = {{Robust Multilinear Principal Component Analysis}},
  author    = {Inoue, Kohei and Hara, Kenji and Urahama, Kiichi},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2009},
  pages     = {591-597},
  doi       = {10.1109/ICCV.2009.5459186},
  url       = {https://mlanthology.org/iccv/2009/inoue2009iccv-robust/}
}