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11th international Joint Conference on Rough Sets, IJCRS 2025

作者： Zou, Jinlin Ma, Yingcang Yang, xiaofei xing, Zhiwei School of Science Xi’an Polytechnic University Xi’an710048 China Xi’an International Science and Technology Cooperation Base for Big Data Analysis and Algorithms Xi’an710048 China

ISBN: (纸本)9783031927430

The core challenge in multi-view feature selection lies in identifying discriminative features that capture both consensus and diversity information. Feature selection methods based on consensus learning have garnered significant attention due to their ability to reveal the common underlying structure across different views. To effectively capture both homogeneous and heterogeneous information across views, this paper proposes a novel multi-view unsupervised feature selection method guided by diversity and consensus structure, referred to as GDCSMuFS. The diversity structure learning across multiple views is crucial for preserving the heterogeneous information in the selected features. Building on this concept, the proposed method begins by incorporating both homogeneous and heterogeneous information. It first learns the multi-view cluster structure matrix through non-negative matrix factorization. Then, multi-view spectral analysis is employed to integrate the cluster structure matrices into a consensus pseudo-label matrix, enabling the extraction of consensus information at the label level. Subsequently, a feature selection matrix is constructed under the shared constraints of consensus labels and graph structures. Finally, sparse constraints are applied to the feature selection matrix, and the model is solved using an alternating iterative algorithm. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method, with comparative results showing its superiority over existing algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Non-negative matrix factorization

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Publisher Correction to: Dynamics of a Stochastic Measles Model with General Incidence Rate and Black–Karasinski Process

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Bulletin of the Malaysian Mathematical sciences Society 2024年第6期47卷 1-2页

作者： Nie, Jiandong Chen, Qiaoling Teng, Zhidong Zhang, Yihan Rifhat, Ramziya School of Science Xi’an Polytechnic University Xi’an People’s Republic of China Xi’an International Science and Technology Cooperation Base for Big Data Analysis and Algorithms Xi’an People’s Republic of China College of Medical Engineering and Technology Xinjiang Medical University Urumqi People’s Republic of China

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