Maximum Correntropy Criterion based Sparse Subspace Learning for Unsupervised Feature Selection

Nan Zhou Yangyang Xu Hong Cheng Zejian Yuan Badong Chen

Numerical Linear Algebra mathscidoc:1912.43154

IEEE Transactions on Circuits and Systems for Video Technology, 29, (2), 404-417, 2017
High-dimensional data contain not only redundancy but also noises produced by the sensors. These noises are usually non-Gaussian distributed. The metrics based on Euclidean distance are not suitable for these situations in general. In order to select the useful features and combat the adverse effects of the noises simultaneously, a robust sparse subspace learning method in unsupervised scenario is proposed in this paper based on the maximum correntropy criterion that shows strong robustness against outliers. Furthermore, an iterative strategy based on half quadratic and an accelerated block coordinate update is proposed. The convergence analysis of the proposed method is also carried out to ensure the convergence to a reliable solution. Extensive experiments are conducted on real-world data sets to show that the new method can filter out the outliers and outperform several state-of-the-art unsupervised
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@inproceedings{nan2017maximum,
  title={Maximum Correntropy Criterion based Sparse Subspace Learning for Unsupervised Feature Selection},
  author={Nan Zhou, Yangyang Xu, Hong Cheng, Zejian Yuan, and Badong Chen},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221112611881797714},
  booktitle={IEEE Transactions on Circuits and Systems for Video Technology},
  volume={29},
  number={2},
  pages={404-417},
  year={2017},
}
Nan Zhou, Yangyang Xu, Hong Cheng, Zejian Yuan, and Badong Chen. Maximum Correntropy Criterion based Sparse Subspace Learning for Unsupervised Feature Selection. 2017. Vol. 29. In IEEE Transactions on Circuits and Systems for Video Technology. pp.404-417. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221112611881797714.
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