IEICE TRANSACTIONS on Information
Shared Latent Embedding Learning for Multi-View Subspace Clustering
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E107-D No.1 pp.148-152
- Publication Date
- 2024/01/01
- Publicized
- 2023/10/17
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2023EDL8044
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Zhaohu LIU
Yantai University
Peng SONG
Yantai University
Jinshuai MU
Yantai University
Wenming ZHENG
Southeast University
Keyword
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Zhaohu LIU, Peng SONG, Jinshuai MU, Wenming ZHENG, "Shared Latent Embedding Learning for Multi-View Subspace Clustering" in IEICE TRANSACTIONS on Information,
vol. E107-D, no. 1, pp. 148-152, January 2024, doi: 10.1587/transinf.2023EDL8044.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8044/_p
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@ARTICLE{e107-d_1_148,
author={Zhaohu LIU, Peng SONG, Jinshuai MU, Wenming ZHENG, },
journal={IEICE TRANSACTIONS on Information},
title={Shared Latent Embedding Learning for Multi-View Subspace Clustering},
year={2024},
volume={E107-D},
number={1},
pages={148-152},
abstract={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.},
keywords={},
doi={10.1587/transinf.2023EDL8044},
ISSN={1745-1361},
month={January},}
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TY - JOUR
TI - Shared Latent Embedding Learning for Multi-View Subspace Clustering
T2 - IEICE TRANSACTIONS on Information
SP - 148
EP - 152
AU - Zhaohu LIU
AU - Peng SONG
AU - Jinshuai MU
AU - Wenming ZHENG
PY - 2024
DO - 10.1587/transinf.2023EDL8044
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E107-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2024
AB - 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.
ER -
English
