Unsupervised Feature Selection via Metric Fusion and Novel Low-Rank Approximation
Unsupervised feature selection aims to derive a compact set of features with desired generalization ability via removing the irrelevant and redundant features, yet challenging due to the unavailability of labels. Works about unsupervised feature selection always need to construct the similarity matr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9895263/ |