Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection

Nan Zhou Yangyang Xu Hong Cheng Jun Fang Witold Pedrycz

Optimization and Control mathscidoc:1912.43145

Pattern Recognition, 53, 87-101, 2016
As we aim at alleviating the curse of high-dimensionality, subspace learning is becoming more popular. Existing approaches use either information about global or local structure of the data, and few studies simultaneously focus on global and local structures as the both of them contain important information. In this paper, we propose a global and local structure preserving sparse subspace learning (GLoSS) model for unsupervised feature selection. The model can simultaneously realize feature selection and subspace learning. In addition, we develop a greedy algorithm to establish a generic combinatorial model, and an iterative strategy based on an accelerated block coordinate descent is used to solve the GLoSS problem. We also provide whole iterate sequence convergence analysis of the proposed iterative algorithm. Extensive experiments are conducted on real-world datasets to show the superiority of the
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@inproceedings{nan2016global,
  title={Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection},
  author={Nan Zhou, Yangyang Xu, Hong Cheng, Jun Fang, and Witold Pedrycz},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221112536423170705},
  booktitle={Pattern Recognition},
  volume={53},
  pages={87-101},
  year={2016},
}
Nan Zhou, Yangyang Xu, Hong Cheng, Jun Fang, and Witold Pedrycz. Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection. 2016. Vol. 53. In Pattern Recognition. pp.87-101. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221112536423170705.
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