Semi-supervised feature selection based on pairwise constraint-guided dual space latent representation learning and double sparse graphs discriminant
Author:
Funder
the National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-04040-y.pdf
Reference42 articles.
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3. Chen L, Lu J (2021) Adaptive graph learning for semi-supervised self-paced classification. Neural Processing Letters
4. Cui J, Zhu Q, Wang D, Li Z (2019) Learning robust latent representation for discriminative regression. Pattern Recogn Lett 117:193–200
5. Ding D, Yang X, Xia F, Ma T, Liu H, Tang C (2020) Unsupervised feature selection via adaptive hypergraph regularized latent representation learning. Neurocomputing 378:79–97
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1. Semi-supervised feature selection by minimum neighborhood redundancy and maximum neighborhood relevancy;Applied Intelligence;2024-06-13
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