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Robust Dual-Graph Regularized Deep Matrix Factorization for Multi-view Clustering

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Abstract

The matrix factorization approaches have been widely applied for multi-view clustering since they can effectively explore complementary information contained in the multi-view data. However, some prior knowledge hidden in multi-view data cannot be fully exploited in existing matrix factorization based multi-view learning approaches. In this paper, we present a robust dual-graph regularized deep matrix factorization (RDDMF) approach for multi-view clustering. Specifically, it integrates the dual-graph regularizers and the sparse constraints into the deep matrix factorization framework. Therefore, the proposed RDDMF approach discovers the geometric structures of both the data and the feature space by adding the dual graph regularization term into deep matrix factorization in each layer. Meanwhile, the sparse constraints are imposed on the coefficient matrix of each layer to improve the robustness of our model. Besides, we design an efficient optimization strategy of the proposed model and give its convergence rate. Numerous experiments on four well-known datasets show our proposed RDDMF approach is superior to several state-of-the-art approaches in multi-view clustering.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant Nos. 61603159, 62162033, U21B2027, 62006097], Yunnan Provincial Major Science and Technology Special Plan Projects [Grant Nos. 202002AD080001, 202103AA080015], Yunnan Foundation Research Projects [Grant Nos. 202101AT070438, 202101BE070001-056], the Natural Science Foundation of Jiangsu Province (Grant No. BK20200593), Excellent Key Teachers of QingLan Project in Jiangsu Province.

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  1. Zhenqiu Shu and Bin Li have contributed equally to this work.

Authors and Affiliations

  1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China

    Zhenqiu Shu, Bin Li & Zhengtao Yu

  2. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, 214000, Jiangsu, China

    Cong Hu & Xiao-Jun Wu

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  1. Zhenqiu Shu
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  2. Bin Li
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  3. Cong Hu
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  4. Zhengtao Yu
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Correspondence to Zhenqiu Shu.

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Shu, Z., Li, B., Hu, C. et al. Robust Dual-Graph Regularized Deep Matrix Factorization for Multi-view Clustering. Neural Process Lett 55, 6067–6087 (2023). https://doi.org/10.1007/s11063-022-11127-7

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