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One-Stage Multi-view Clustering with Hierarchical Attributes Extraction

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Abstract

Multi-view clustering (MVC) has received significant attention, and obtained praiseworthy performance improvement in comparison with signal-view clustering, since it can effectively take advantage of the underlying correlation and structure information of multi-view data. However, existing methods only utilize signal-layer mapping to exploit clustering information, and ignore the underlying hierarchical attribute information in complex and interleaved multi-view data. In this work, we propose a novel MVC method, one-stage multi-view clustering with hierarchical attributes extracting (OS-HAE), to exploit the underlying hierarchical attributes for MVC. Specifically, we learn multiple latent representations from each view by a novel deep matrix factorization (DMF) framework with a layer-wise scheme, so that the learned representations can contain the hierarchical attribute information of original multi-view data. In addition, the samples from the same clusters but from different views are forced to be closer, and samples from different cluster are away from each other in the latent low-dimensional space. Furthermore, we introduce local manifold learning to guide DMF, such that the deepest representations can preserve structure information of original data. Meanwhile, a novel auto-weighted spectral rotating fusion (ASRF) paradigm is proposed to obtain the final clustering indicator matrix directly, so that OS-HAE can avoid obtaining suboptimal results caused by a two-stage strategy. Then, an alternate algorithm is designed to solve the objective function. Experimental results on six datasets demonstrate the advancement and effectiveness of the proposed OS-HAE. Consequently, the proposed method can effectively exploit the hierarchical information of multi-view to improve clustering performance.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 62106209), the Public Welfare Technology Application Research Project of Zhejiang Province, China (Grant No. LGF21F020003), the Open Project Program of the State Key Lab of CAD &CG (Grant No. A2217), and the Natural Science Foundation of Southwest University of Science and Technology (Grant No. 22zx7101).

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Authors and Affiliations

  1. School of National Defence Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, China

    Yong Mi & Xiaojian You

  2. School of Computer and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 610097, China

    Jian Dai

  3. Southwest Automation Research Institute, China South Industries Group Corporation, Mianyang, 621000, China

    Zhenwen Ren

  4. College of Media Engineering, Communication University of Zhejiang, Zhejiang, 310018, China

    Yanlong Wang

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  1. Yong Mi
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  2. Jian Dai
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Correspondence to Zhenwen Ren or Xiaojian You.

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Mi, Y., Dai, J., Ren, Z. et al. One-Stage Multi-view Clustering with Hierarchical Attributes Extraction. Cogn Comput 15, 552–564 (2023). https://doi.org/10.1007/s12559-022-10060-0

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