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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Published in | IEICE Transactions on Information and Systems Vol. E107.D; no. 1; pp. 148 - 152 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Tokyo
The Institute of Electronics, Information and Communication Engineers
01.01.2024
Japan Science and Technology Agency |
Subjects | |
Online Access | Get full text |
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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. |
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ISSN: | 0916-8532 1745-1361 |
DOI: | 10.1587/transinf.2023EDL8044 |