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Auto-weighted two-dimensional principal component analysis with robust outliers

  • Northwestern Polytechnical University Xian

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

16 引用 (Scopus)

摘要

Two-dimensional principal component analysis (2DPCA) serves as an efficient approach for both dimensionality reduction and high-quality reconstruction. However, conventional 2DPCA method is sensitive to the outliers such that associated results could be compromised. To strengthen the robustness of conventional 2DPCA method, we try to propose a novel robust two-dimensional principal component analysis with optimal mean (R2DPCA-OM) method to automatically achieve the optimal mean. Besides, the experimental results illustrate that the proposed R2DPCA-OM method could obtain the optimal subspaces and mean, such that dimensionality is reduced with less reconstruction error. Consequently, superiority and effectiveness of the proposed R2DPCA-OM method could be verified analytically and empirically.

源语言英语
主期刊名2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
页6065-6069
页数5
ISBN(电子版)9781509041176
DOI
出版状态已出版 - 16 6月 2017
活动2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, 美国
期限: 5 3月 2017 → 9 3月 2017

丛书

姓名ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷版)1520-6149

会议

会议2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
国家/地区美国
市New Orleans
时期5/03/17 → 9/03/17

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