Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (2): 145-157.doi: 10.11896/jsjkx.231100173

• Database & Big Data & Data Science • Previous Articles     Next Articles

Multi-view Clustering Based on Cross-structural Feature Selection and Graph Cycle AdaptiveLearning

XIN Yongjie1, CAI Jianghui1,3, HE Yanting1, SU Meihong1,2, SHI Chenhui1, YANG Haifeng1,2   

  1. 1 School of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China
    2 Shanxi Key Laboratory of Big Data Analysis and Parallel Computing,Taiyuan 030024,China
    3 School of Computer Science and Technology,North University of China,Taiyuan 030051,China
  • Received:2023-12-27 Revised:2024-04-23 Online:2025-02-15 Published:2025-02-17
  • About author:XIN Yongjie,born in 1998,postgra-duate.His main research interests include data mining and machine lear-ning.
    YANG Haifeng,born in 1980,professor,Ph.D supervisor,is a senior member of CCF(No.74391S).His main research interests include big data mi-ning,machine learning and spectral ana-lysis of extragalactic galaxies.
  • Supported by:
    National Natural Science Foundation of China(U1931209),Projects of Science and Technology Cooperation and Exchange of Shanxi Province(202204041101037,202204041101033) and Projects of Taiyuan University of Science and Technology Graduate Education Innovation(BY2023015).

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Abstract: Most of the existing graph adaptive learning methods rely on high-dimensional raw data and inevitably have phenomena such as noise or missing information in the data,resulting in the inability to accurately select the important feature information in the high-dimensional data,in addition to ignoring the structural relevance of the multi-view representations in the feature selection process.To tackle the above problems,a multi-view clustering method(MLFS-GCA) based on cross-structural feature selection and graph cycle adaptive learning is proposed.First,a cross-structural feature selection framework is designed.By jointly learning the spatial structure characteristics of multi-view representations and the consistency of the clustering structure,the high-dimensional data is projected into a low-dimensional linear subspace,and the low-dimensional feature representation is learned with the assistance of view-specific basis matrix and consistent clustering structures.Second,a graph cycle adaptive learning module is proposed.The k nearest neighbors in the projection space are selected by the k-nearest neighbor(k-NN) method,and the similar structures are optimized cyclically in concert with matrix low-rank learning.Eventually,the shared sparse similarity matrix for clustering task is learned.The superiority of graph cycle adaptive learning in multi-view clustering is demonstrated through extensive experiments on various real multi-view datasets.

Key words: Multi-view clustering, Graph cycle adaptive learning, Cross-structural feature selection, k-NN, Matrix low-rank learning

CLC Number: 

  • TP181
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