RRNMF-MAGL: Robust regularization non-negative matrix factorization with multi-constraint adaptive graph learning for dimensionality reduction

Yi, Yugen; Lai, Shumin; Li, Shicheng; Dai, Jiangyan; Wang, Wenle; Wang, Jianzhong

doi:10.1016/j.ins.2023.119029

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…The accuracy rate [63] is employed to measure the performance of SFS-AGGL on the classification task, which is represented as:…”

Section: Evaluation Metricmentioning

confidence: 99%

SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

Yi,

Zhang,

Zhang

et al. 2024

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As the feature dimension of data continues to expand, the task of selecting an optimal subset of features from a pool of limited labeled data and extensive unlabeled data becomes more and more challenging. In recent years, some semi-supervised feature selection methods (SSFS) have been proposed to select a subset of features, but they still have some drawbacks limiting their performance, for e.g., many SSFS methods underutilize the structural distribution information available within labeled and unlabeled data. To address this issue, we proposed a semi-supervised feature selection method based on an adaptive graph with global and local constraints (SFS-AGGL) in this paper. Specifically, we first designed an adaptive graph learning mechanism that can consider both the global and local information of samples to effectively learn and retain the geometric structural information of the original dataset. Secondly, we constructed a label propagation technique integrated with the adaptive graph learning in SFS-AGGL to fully utilize the structural distribution information of both labeled and unlabeled data. The proposed SFS-AGGL method is validated through classification and clustering tasks across various datasets. The experimental results demonstrate its superiority over existing benchmark methods, particularly in terms of clustering performance.

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“…The accuracy rate [63] is employed to measure the performance of SFS-AGGL on the classification task, which is represented as:…”

Section: Evaluation Metricmentioning

confidence: 99%

SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

Yi,

Zhang,

Zhang

et al. 2024

Information

Self Cite

View full text Add to dashboard Cite

show abstract

Cluster structure augmented deep nonnegative matrix factorization with low-rank tensor learning

Zhong,

Wu,

Huang

et al. 2024

Information Sciences

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RRNMF-MAGL: Robust regularization non-negative matrix factorization with multi-constraint adaptive graph learning for dimensionality reduction

Cited by 2 publications

References 40 publications

SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

SFS-AGGL: Semi-Supervised Feature Selection Integrating Adaptive Graph with Global and Local Information

Cluster structure augmented deep nonnegative matrix factorization with low-rank tensor learning

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