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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Veröffentlicht in:IEEE transactions on cybernetics Jg. 52; H. 6; S. 5522 - 5534
Hauptverfasser: Yuan, Aihong, You, Mengbo, He, Dongjian, Li, Xuelong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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 between the data samples or use pseudolabel matrix learning to learn the mapping between the data and labels. Furthermore, the existing methods have tried to add constraints to either of these two modules to reduce the redundancy, but no prior literature embeds them into a joint model to select the most representative features by the computed top ranking scores. To address the aforementioned issue, this article presents a novel UFS method via a convex non-negative matrix factorization with an adaptive graph constraint (CNAFS). Through convex matrix factorization with adaptive graph constraint, it can dig up the correlation between the data and keep the local manifold structure of the data. To our knowledge, it is the first work that integrates pseudo label matrix learning into the self-expression module and optimizes them simultaneously for the UFS solution. Besides, two different manifold regularizations are constructed for the pseudolabel matrix and the encoding matrix to keep the local geometrical structure. Eventually, extensive experiments on the benchmark datasets are conducted to prove the effectiveness of our method. The source code is available at: https://github.com/misteru/CNAFS .
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2020.3034462