Convex Non-Negative Matrix Factorization With Adaptive Graph for Unsupervised Feature Selection

Unsupervised feature selection (UFS) aims to remove the redundant information and select the most representative feature subset from the original data, so it occupies a core position for high-dimensional data preprocessing. Many proposed approaches use self-expression to explore the correlation betw...

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Bibliographic Details
Published in:	IEEE transactions on cybernetics Vol. 52; no. 6; pp. 5522 - 5534
Main Authors:	Yuan, Aihong, You, Mengbo, He, Dongjian, Li, Xuelong
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.06.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptive graph constraint Analytical models Computational modeling Encoding Factorization Feature extraction Feature selection Learning manifold structure Manifolds Modules non-negative matrix factorization (NMF) Optimization Redundancy Self expression Source code Task analysis unsupervised feature selection (UFS)
ISSN:	2168-2267, 2168-2275, 2168-2275
Online Access:	Get full text
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