Robust Unsupervised Feature Selection via Multi-Group Adaptive Graph Representation

Unsupervised feature selection can play an important role in addressing the issue of processing massive unlabelled high-dimensional data in the domain of machine learning and data mining. This paper presents a novel unsupervised feature selection method, referred to as Multi-Group Adaptive Graph Rep...

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Vydáno v:	IEEE transactions on knowledge and data engineering Ročník 35; číslo 3; s. 3030 - 3044
Hlavní autoři:	You, Mengbo, Yuan, Aihong, Zou, Min, He, Dongjian, Li, Xuelong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.03.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptation models adaptive graph constraint alternative iterative algorithm Clustering Data mining Data models Faces Feature extraction Feature selection Graph representations Graphical representations Iterative algorithms Iterative methods Machine learning multiple groups Optimization Similarity Source code Sparse matrices Unsupervised feature selection
ISSN:	1041-4347, 1558-2191
On-line přístup:	Získat plný text
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