Robust multilinear principal component analysis

Kohei Inoue, Kenji Hara, Kiichi Urahama

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

12 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトル2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
ページ591-597
ページ数7
DOI
出版ステータス出版済み - 12 1 2009
イベント12th International Conference on Computer Vision, ICCV 2009 - Kyoto, 日本
継続期間: 9 29 200910 2 2009

出版物シリーズ

名前Proceedings of the IEEE International Conference on Computer Vision

その他

その他12th International Conference on Computer Vision, ICCV 2009
Country日本
CityKyoto
Period9/29/0910/2/09

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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