Shared Latent Embedding Learning for Multi-View Subspace Clustering

DOI Web Site 参考文献13件
  • LIU Zhaohu
    School of Computer and Control Engineering, Yantai University
  • SONG Peng
    School of Computer and Control Engineering, Yantai University
  • MU Jinshuai
    School of Computer and Control Engineering, Yantai University
  • ZHENG Wenming
    Key Laboratory of Child Development and Learning Science, Ministry of Education (Southeast University), Southeast University

抄録

<p>Most existing multi-view subspace clustering approaches only capture the inter-view similarities between different views and ignore the optimal local geometric structure of the original data. To this end, in this letter, we put forward a novel method named shared latent embedding learning for multi-view subspace clustering (SLE-MSC), which can efficiently capture a better latent space. To be specific, we introduce a pseudo-label constraint to capture the intra-view similarities within each view. Meanwhile, we utilize a novel optimal graph Laplacian to learn the consistent latent representation, in which the common manifold is considered as the optimal manifold to obtain a more reasonable local geometric structure. Comprehensive experimental results indicate the superiority and effectiveness of the proposed method.</p>

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