[1]CHEN Tong,CHEN Xiuhong.Feature self-representation and graph regularization for robust unsupervised feature selection[J].CAAI Transactions on Intelligent Systems,2022,17(2):286-294.[doi:10.11992/tis.202012043]
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Feature self-representation and graph regularization for robust unsupervised feature selection
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
17
Number of periods:
2022 2
Page number:
286-294
Column:
学术论文—机器学习
Public date:
2022-03-05
- Title:
- Feature self-representation and graph regularization for robust unsupervised feature selection
- Keywords:
- feature selection; robust; graph Laplacian; feature self-representation; low-rank constraint; unsupervised; ${L_{2; 1}}$-norm; dimension reduction
- CLC:
- TP181
- DOI:
- 10.11992/tis.202012043
- Abstract:
- In order to reveal the global structure of data and preserve its local structure, this paper proposes a new unsupervised feature selection (UFS) method, which puts feature self-representation and graph regularization into the same framework. Specifically, the model uses the self-representation of the features to represent each feature through other features for preserving the local structure of the features. An ${L_{2, 1}}$-norm based graph regularization term is used to reduce the effect of noisy data on feature selection while preserving the local geometric structure. Furthermore, the model uses a low-rank constraint on the weight matrix to preserve the global structure. Experiments on six different public datasets show that the algorithm is clearly superior to the other five algorithms, which demonstrates the effectiveness of the proposed UFS framework.
- References:
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