Discriminative multi-label feature selection with adaptive graph diffusion

Ma, Jiajun; Xu, Fei; Rong, Xiaofeng

doi:10.1016/j.patcog.2023.110154

Cited by 15 publications

(1 citation statement)

References 29 publications

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“…Recently, graph-based methods, such as spectral clustering, graph learning and hypergraph learning, have played an important role in machine learning due to their ability to encode similarity relationships among data. Ma et al [34] proposed a feature selection method named discriminative multi-label feature selection with adaptive graph diffusion, and the graph embedding learning framework is constructed with adaptive graph diffusion to uncover a latent subspace that preserves the higher-order structure information. Zhang et al [35] proposed a novel unsupervised feature selection via adaptive graph learning and constraint.…”

Section: Related Workmentioning

confidence: 99%

Multi-Target Feature Selection with Adaptive Graph Learning and Target Correlations

Zhou,

2024

Mathematics

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In this paper, we present a novel multi-target feature selection algorithm that incorporates adaptive graph learning and target correlations. Specifically, our proposed approach introduces the low-rank constraint on the regression matrix, allowing us to model both inter-target and input–output relationships within a unified framework. To preserve the similarity structure of the samples and mitigate the influence of noise and outliers, we learn a graph matrix that captures the induced sample similarity. Furthermore, we introduce a manifold regularizer to maintain the global target correlations, ensuring the preservation of the overall target relationship during subsequent learning processes. To solve the final objective function, we also propose an optimization algorithm. Through extensive experiments on eight real-world datasets, we demonstrate that our proposed method outperforms state-of-the-art multi-target feature selection techniques.

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Section: Related Workmentioning

confidence: 99%