Unsupervised feature selection through combining graph learning and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-norm constraint

Zhu, Peican; Hou, Xin; Tang, Keke; Liu, Yang; Zhao, Yin-Ping; Wang, Zhen

doi:10.1016/j.ins.2022.11.156

Cited by 30 publications

(8 citation statements)

References 41 publications

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“…Subsequently, numerous graph construction methods based on data correlation have been presented, including L 1 graph [27], low-rank representation (LRR) [28], local structure learning [29], and sparse subspace clustering (SSC) [30], to construct high-quality graphs. The above-mentioned graph construction methods are integrated into FS models, proposing a large number of improvements for feature selection [31][32][33][34][35][36][37]. However, the processes of adaptive graph construction and FS in the above-mentioned methods are independent of each other, so the influence of graph construction on the FS process is limited.…”

Section: Introductionmentioning

confidence: 99%

“…construct high-quality graphs. The above-mentioned graph construction method integrated into FS models, proposing a large number of improvements for feature tion [31][32][33][34][35][36][37]. However, the processes of adaptive graph construction and FS above-mentioned methods are independent of each other, so the influence of grap struction on the FS process is limited.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…It is intuitive to ask the question: Whether it is also effective to attack the features of middle layers? Various scholars devote their efforts to tackling this question [39,40]. Zhou et al presented the Transferable Adversarial Perturbations (TAP) to improve the transferability through increasing the feature distances between the original image and its adversarial example in the intermediate layers [41].…”

Section: Related Workmentioning

confidence: 99%

SGMA: a novel adversarial attack approach with improved transferability

Zhu

Hong²,

et al. 2023

Complex Intell. Syst.

Self Cite

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Deep learning models are easily deceived by adversarial examples, and transferable attacks are crucial because of the inaccessibility of model information. Existing SOTA attack approaches tend to destroy important features of objects to generate adversarial examples. This paper proposes the split grid mask attack (SGMA), which reduces the intensity of model-specific features by split grid mask transformation, effectively highlighting the important features of the input image. Perturbing these important features can guide the development of adversarial examples in a more transferable direction. Specifically, we introduce the split grid mask transformation into the input image. Due to the vulnerability of model-specific features to image transformations, the intensity of model-specific features decreases after aggregation while the intensities of important features remain. The generated adversarial examples guided by destroying important features have excellent transferability. Extensive experimental results demonstrate the effectiveness of the proposed SGMA. Compared to the SOTA attack approaches, our method improves the black-box attack success rates by an average of 6.4% and 8.2% against the normally trained models and the defense ones respectively.

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“…A graph-theoretic method based on a two-step procedure that combines filter and wrapper methods was proposed in [13] to classify micro-array data. Beyond single-label learning feature selection has also been applied in other contexts such as multi-label, multi-view, unsupervised, and label distribution learning [31] , [40] , [53] , [55] .…”

Section: Literaturementioning

confidence: 99%

Nested ensemble selection: An effective hybrid feature selection method

Kamalov,

Sulieman,

Moussa

et al. 2023

Heliyon

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Unsupervised feature selection through combining graph learning and ℓ2,0-norm constraint

Cited by 30 publications

References 41 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

SGMA: a novel adversarial attack approach with improved transferability

Nested ensemble selection: An effective hybrid feature selection method

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