The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
La plupart des approches de clustering de sous-espaces multi-vues existantes capturent uniquement les similitudes entre les différentes vues et ignorent la structure géométrique locale optimale des données d'origine. À cette fin, dans cette lettre, nous proposons une nouvelle méthode appelée apprentissage par intégration latente partagée pour le clustering de sous-espaces multi-vues (SLE-MSC), qui peut capturer efficacement un meilleur espace latent. Pour être plus précis, nous introduisons une contrainte de pseudo-étiquette pour capturer les similitudes intra-vue au sein de chaque vue. Parallèlement, nous utilisons un nouveau graphe optimal Laplacien pour apprendre la représentation latente cohérente, dans laquelle la variété commune est considérée comme la variété optimale pour obtenir une structure géométrique locale plus raisonnable. Des résultats expérimentaux complets indiquent la supériorité et l’efficacité de la méthode proposée.
Zhaohu LIU
Yantai University
Peng SONG
Yantai University
Jinshuai MU
Yantai University
Wenming ZHENG
Southeast University
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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 -