Shared Latent Embedding Learning for Multi-View Subspace Clustering

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-...

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Bibliographic Details
Published inIEICE Transactions on Information and Systems Vol. E107.D; no. 1; pp. 148 - 152
Main Authors LIU, Zhaohu, SONG, Peng, MU, Jinshuai, ZHENG, Wenming
Format Journal Article
LanguageEnglish
Published Tokyo The Institute of Electronics, Information and Communication Engineers 01.01.2024
Japan Science and Technology Agency
Subjects
Clustering
Embedding
graph Laplacian
latent representation
Learning
multi-view learning
Similarity
subspace clustering
Subspaces
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Summary: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.
ISSN:0916-8532
1745-1361
DOI:10.1587/transinf.2023EDL8044