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<p>Incomplete Multi-view Clustering (IMC) has attracted increasing attention in recent years. However, most existing IMC approaches may suffer from the following problems: (a) they may neglect to learn partial-consensus knowledge, and this knowledge widely exists in incomplete multi-view data; and (b) they may fail to fully explore the knowledge in views. To address the above issues, we propose a Tensor-constrained Consensus, Partial-consensus and Specificity Components learning framework for Incomplete Multi-view Clustering (TCPS-IMC). TCPS-IMC includes two components: (a) Views Division Learning (VDL): The views are divided into consensus, partial-consensus and specific parts to separately learn incomplete multi-view data; and (b) Knowledge Enhancement Learning (KEL): the view low-rank is guaranteed by imposing the tensor nuclear norm on views, the important features of partial-consensus knowledge are extracted by minimizing the Hadamard product between partial-consensus parts, and the more specific knowledge is learned by imposing the <i>F</i>-norm on specific parts. By TCPS-IMC, incomplete multi-view data are sufficiently learned, and the knowledge in views are explored as much as possible. In the incomplete multi-view clustering, TCPS-IMC achieves higher average ACC than the suboptimal method by <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(4.64\%\)</EquationSource> </InlineEquation> under the 50% pairing ratio, and it validates the effectiveness of TCPS-IMC. The source code of TCPS-IMC is available at <a href="https://github.com/GDUT-zhangjinchao/TCPS-IMC">https://github.com/GDUT-zhangjinchao/TCPS-IMC</a>.</p>

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Tensor-constrained consensus, partial-consensus and specificity components learning framework for incomplete multi-view clustering

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Shaohua Teng,
  • Jinchao Zhang,
  • Luyao Teng,
  • Xiaoqiong Long,
  • Wei Zhang,
  • Naiqi Wu,
  • Zefeng Zheng

摘要

Incomplete Multi-view Clustering (IMC) has attracted increasing attention in recent years. However, most existing IMC approaches may suffer from the following problems: (a) they may neglect to learn partial-consensus knowledge, and this knowledge widely exists in incomplete multi-view data; and (b) they may fail to fully explore the knowledge in views. To address the above issues, we propose a Tensor-constrained Consensus, Partial-consensus and Specificity Components learning framework for Incomplete Multi-view Clustering (TCPS-IMC). TCPS-IMC includes two components: (a) Views Division Learning (VDL): The views are divided into consensus, partial-consensus and specific parts to separately learn incomplete multi-view data; and (b) Knowledge Enhancement Learning (KEL): the view low-rank is guaranteed by imposing the tensor nuclear norm on views, the important features of partial-consensus knowledge are extracted by minimizing the Hadamard product between partial-consensus parts, and the more specific knowledge is learned by imposing the F-norm on specific parts. By TCPS-IMC, incomplete multi-view data are sufficiently learned, and the knowledge in views are explored as much as possible. In the incomplete multi-view clustering, TCPS-IMC achieves higher average ACC than the suboptimal method by \(4.64\%\) under the 50% pairing ratio, and it validates the effectiveness of TCPS-IMC. The source code of TCPS-IMC is available at https://github.com/GDUT-zhangjinchao/TCPS-IMC.