Multi-view Spectral Clustering via Integrating Label and Data Graph Learning

Sally El Hajjar, Fadi Dornaika, Fahed Abdallah, Hichem Omrani

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Résumé

Nowadays, one-step multi-view clustering algorithms attract many interests. The main issue of multi-view clustering approaches is how to combine the information extracted from the available views. A popular approach is to use view-based graphs and/or a consensus graph to describe the different views. We introduce a novel one-step graph-based multi-view clustering approach in this study. Our suggested method, in contrast to existing graph-based one-step clustering methods, provides two major novelties to the method called Nonnegative Embedding and Spectral Embedding (NESE) proposed in the recent paper [1]. To begin, we use the cluster label correlation to create an additional graph in addition to the graphs associated with the data space. Second, the cluster-label matrix is constrained by adopting some restrictions to make it more consistent. The effectiveness of the proposed method is demonstrated by experimental results on many public datasets.

langue originaleAnglais
titreImage Analysis and Processing – ICIAP 2022 - 21st International Conference, 2022, Proceedings
rédacteurs en chefStan Sclaroff, Cosimo Distante, Marco Leo, Giovanni M. Farinella, Federico Tombari
EditeurSpringer Science and Business Media Deutschland GmbH
Pages109-120
Nombre de pages12
ISBN (imprimé)9783031064326
Les DOIs
étatPublié - 2022
Evénement21st International Conference on Image Analysis and Processing, ICIAP 2022 - Lecce, Italie
Durée: 23 mai 202227 mai 2022

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13233 LNCS
ISSN (imprimé)0302-9743
ISSN (Electronique)1611-3349

Une conférence

Une conférence21st International Conference on Image Analysis and Processing, ICIAP 2022
Pays/TerritoireItalie
La villeLecce
période23/05/2227/05/22

Une note bibliographique

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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