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<p>Subspace clustering methods are increasingly favored in engineering applications because of their unsupervised nature. However, their performance in processing multi-view nonlinear data in unsupervised systems is often affected by the following three factors: (1) how to use data with valid information from each view in an appropriate way to avoid introducing irrelevant information; (2) how to improve incomplete data information in each view; and (3) how to balance the contributions of different views to ensure that the most informative view receives adequate attention. In this research, we propose a multi-kernel RPCA with graph regulation to prevent irrelevant information from being introduced into the clustering process of target objects. We also design a multi-view data completion framework with enhanced low-rank constraint to improve the quality of incomplete data in each view. Furthermore, we integrate these components into a unified framework and emphasize the effective synergy and complementary integration of information across different views. A large number of experiments in unsupervised systems, including multi-view data clustering, color video motion segmentation, invisible light video motion segmentation and color image segmentation, demonstrate the feasibility of our proposed method.</p>

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Multi-view kernel subspace clustering with adaptive information completion and fusion for unsupervised systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月21日

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  • Li Guo,
  • Zhigui Liu,
  • Jiao Bao,
  • Qian Wang

摘要

Subspace clustering methods are increasingly favored in engineering applications because of their unsupervised nature. However, their performance in processing multi-view nonlinear data in unsupervised systems is often affected by the following three factors: (1) how to use data with valid information from each view in an appropriate way to avoid introducing irrelevant information; (2) how to improve incomplete data information in each view; and (3) how to balance the contributions of different views to ensure that the most informative view receives adequate attention. In this research, we propose a multi-kernel RPCA with graph regulation to prevent irrelevant information from being introduced into the clustering process of target objects. We also design a multi-view data completion framework with enhanced low-rank constraint to improve the quality of incomplete data in each view. Furthermore, we integrate these components into a unified framework and emphasize the effective synergy and complementary integration of information across different views. A large number of experiments in unsupervised systems, including multi-view data clustering, color video motion segmentation, invisible light video motion segmentation and color image segmentation, demonstrate the feasibility of our proposed method.