Corpus ID: 29153031

Two-Dimensional PCA with F-Norm Minimization

@inproceedings{Wang2017TwoDimensionalPW,
  title={Two-Dimensional PCA with F-Norm Minimization},
  author={Qianqian Wang and Quanxue Gao},
  booktitle={AAAI},
  year={2017}
}
Two-dimensional principle component analysis (2DPCA) has been widely used for face image representation and recognition. But it is sensitive to the presence of outliers. To alleviate this problem, we propose a novel robust 2DPCA, namely 2DPCA with F-norm minimization (F-2DPCA), which is intuitive and directly derived from 2DPCA. In F-2DPCA, distance in spatial dimensions (attribute dimensions) is measured in F-norm, while the summation over different data points uses 1-norm. Thus it is robust… Expand
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