Scalable Multi-view Regression Clustering for Large-scale Data
Zhao, Xiaowei Fan, Jie Chang, Xiaojun Nie, Feiping Zhang, Qiang Guo, Jun
Zhao, Xiaowei
Fan, Jie
Chang, Xiaojun
Nie, Feiping
Zhang, Qiang
Guo, Jun
Supervisor
Department
Computer Vision
Embargo End Date
Type
Journal Article
Date
2025
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Language
English
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Abstract
In recent years, unsupervised linear regression has attracted attention for its ability to directly capture the mapping relationship between samples and targets. However, existing algorithms can only utilize limited information from a single view, which often leads to unsatisfactory results. To address this problem, we propose a regression clustering model based on multi-view information fusion, called Scalable Multi-view Regression Clustering. This model consists of two parts: intra-view information fusion and inter-view information fusion. In the first part, to capture the local correlations among samples, we propose constructing view-specific bipartite graphs. Unlike traditional single-view and multi-view clustering algorithms, we treat the weights of the bipartite graph as additional features of the samples, thereby directly incorporating the local manifold structure of the samples at the feature level. Furthermore, since the original features of the samples also contain valuable information, we perform unsupervised linear regression separately on the samples represented by the original features and those represented by the bipartite graph weights in each view. The results are then integrated in a weighted manner. In the second part, we propose adaptively weighting the clustering results from each view to capture complementary information across views, thereby enhancing clustering performance. This strategy not only avoids the bipartite graph alignment issue in multi-view clustering but also enables clustering with linear time complexity, making it effective for handling large-scale data. An iterative optimization algorithm is developed to update all variables alternately. Experiments conducted on benchmark datasets demonstrate the superiority of our proposed model.
Citation
X. Zhao, J. Fan, X. Chang, F. Nie, Q. Zhang and J. Guo, "Scalable Multi-view Regression Clustering for Large-scale Data," in IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2025.3546973.
Source
IEEE Transactions on Circuits and Systems for Video Technology
Conference
Keywords
Fast multi-view regression clustering, Bipartite graph, Intra-view information fusion, Inter-view information fusion
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Publisher
IEEE