Auto-weighted two-dimensional principal component analysis with robust outliers
Rui Zhang, Feiping Nie, Xuelong Li
Abstract
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.
BibTeX
@inproceedings{icassp2017_autoweightedtwod,
title = {Auto-weighted two-dimensional principal component analysis with robust outliers},
author = {Rui Zhang and Feiping Nie and Xuelong Li},
booktitle = {ICASSP 2017},
year = {2017}
}