Robust multilinear principal component analysis

Kohei Inoue, Kenji Hara, Kiichi Urahama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
Number of pages7
Publication statusPublished - Dec 1 2009
Event12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan
Duration: Sep 29 2009Oct 2 2009

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


Other12th International Conference on Computer Vision, ICCV 2009

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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