Nonnegative graph embedding induced unsupervised feature selection
Recently, many unsupervised feature selection (UFS) methods have been developed due to their effectiveness in selecting valuable features to improve and accelerate the subsequent learning tasks. However, most existing UFS methods suffer from the following three drawbacks: (1) They usually ignore the...
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| Published in: | Expert systems with applications Vol. 282; p. 127664 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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05.07.2025
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| ISSN: | 0957-4174 |
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| Abstract | Recently, many unsupervised feature selection (UFS) methods have been developed due to their effectiveness in selecting valuable features to improve and accelerate the subsequent learning tasks. However, most existing UFS methods suffer from the following three drawbacks: (1) They usually ignore the nonnegative attribute of feature when conducting feature selection, which inevitably loses partial information; (2) Most adopt a separate strategy to rank all features and then select the first k features, which introduces an additional parameter and often obtains suboptimal results; (3) Most generally confront the problem of high time-consuming. To tackle the previously mentioned shortage, we present a novel UFS method, i.e., Nonnegative Graph Embedding Induced Unsupervised Feature Selection, which considers nonnegative feature attributes and selects informative feature subsets in a one-step way. Specifically, the raw data are projected into a low-dimensional subspace, where the learned low-dimensional representation keeps a nonnegative attribute. Then, a novel scheme is designed to preserve the local geometric structure of the original data, and ℓ2,0 norm is introduced to guide feature selection without ranking and selecting processes. Finally, we design a high-efficiency solution strategy with low computational complexity, and experiments on real-life datasets verify the efficiency and advancement compared with advanced UFS methods.
•A learning scheme is designed to preserve local structure and nonnegative attributes.•ℓ2,0-norm is adopted to avoid introducing additional parameters and selection steps.•A high-efficiency strategy with low computational complexity is designed. |
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| AbstractList | Recently, many unsupervised feature selection (UFS) methods have been developed due to their effectiveness in selecting valuable features to improve and accelerate the subsequent learning tasks. However, most existing UFS methods suffer from the following three drawbacks: (1) They usually ignore the nonnegative attribute of feature when conducting feature selection, which inevitably loses partial information; (2) Most adopt a separate strategy to rank all features and then select the first k features, which introduces an additional parameter and often obtains suboptimal results; (3) Most generally confront the problem of high time-consuming. To tackle the previously mentioned shortage, we present a novel UFS method, i.e., Nonnegative Graph Embedding Induced Unsupervised Feature Selection, which considers nonnegative feature attributes and selects informative feature subsets in a one-step way. Specifically, the raw data are projected into a low-dimensional subspace, where the learned low-dimensional representation keeps a nonnegative attribute. Then, a novel scheme is designed to preserve the local geometric structure of the original data, and ℓ2,0 norm is introduced to guide feature selection without ranking and selecting processes. Finally, we design a high-efficiency solution strategy with low computational complexity, and experiments on real-life datasets verify the efficiency and advancement compared with advanced UFS methods.
•A learning scheme is designed to preserve local structure and nonnegative attributes.•ℓ2,0-norm is adopted to avoid introducing additional parameters and selection steps.•A high-efficiency strategy with low computational complexity is designed. |
| ArticleNumber | 127664 |
| Author | Yuan, Zhong Horng, Shi-Jinn Li, Tianrui Mi, Yong Luo, Chuan Chen, Hongmei |
| Author_xml | – sequence: 1 givenname: Yong surname: Mi fullname: Mi, Yong email: miyong@my.swjtu.edu.cn organization: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, China – sequence: 2 givenname: Hongmei orcidid: 0000-0002-7225-5577 surname: Chen fullname: Chen, Hongmei email: hmchen@swjtu.edu.cn organization: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, China – sequence: 3 givenname: Zhong orcidid: 0000-0002-7456-4445 surname: Yuan fullname: Yuan, Zhong email: yuanzhong@scu.edu.cn organization: College of Computer Science, Sichuan University, Chengdu 610065, China – sequence: 4 givenname: Chuan surname: Luo fullname: Luo, Chuan email: cluo@scu.edu.cn organization: College of Computer Science, Sichuan University, Chengdu 610065, China – sequence: 5 givenname: Shi-Jinn surname: Horng fullname: Horng, Shi-Jinn email: horngsj@asia.edu.tw organization: Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan – sequence: 6 givenname: Tianrui orcidid: 0000-0001-7780-104X surname: Li fullname: Li, Tianrui email: trli@swjtu.edu.cn organization: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, China |
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| Keywords | Unsupervised feature selection One-step way ℓ2,0norm Nonnegative attribute Low computational complexity |
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