Unsupervised Feature Selection With Constrained ℓ₂,₀-Norm and Optimized Graph

In this article, we propose a novel feature selection approach, named unsupervised feature selection with constrained <inline-formula> <tex-math notation="LaTeX">\ell _{2,0} </tex-math></inline-formula>-norm (row-sparsity constrained) and optimized graph (RSOGFS), w...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 33; H. 4; S. 1702 - 1713
Hauptverfasser:	Nie, Feiping, Dong, Xia, Tian, Lai, Wang, Rong, Li, Xuelong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.04.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Approximation Approximation algorithms Computer applications Feature extraction Feature selection group feature selection Image color analysis Manifolds Mathematical analysis Noise measurement norm Objective function Optimization optimized graph Regularization Similarity Sparse matrices Sparsity unsupervised learning
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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