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

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… (More)
DOI: 10.1109/ICASSP.2017.7953321

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Cite this paper

@article{Zhang2017AutoweightedTP, title={Auto-weighted two-dimensional principal component analysis with robust outliers}, author={Rui Zhang and Feiping Nie and Yanwei Pang}, journal={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year={2017}, pages={6065-6069} }