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Communication dans un congrès

Joint graph based embedding and feature weighting for image classification

Ruifeng Zhu 1, 2 Fadi Dornaika 3, 2, * Yassine Ruichek 1
* Auteur correspondant
1 Le2i - Laboratoire d'Electronique, d'Informatique et d'Image [EA 7508]
2 Computer Science Department [San Sebastian]
3 Ikerbasque - Basque Foundation for Science
Abstract : The graph-based embedding is an effective and useful method in reducing the dimension and extracting relevant data. This paper introduces a framework for classifying high dimensional data via a joint graph-based embedding and weighting method which could be used in semi-supervised or supervised learning. We design on effective optimization algorithm to solve the objective function. Experiments on image classification show that our proposed method can have a performance that is better than that of many state-of-the-art methods including linear and nonlinear methods.
Keywords : Graph-based embedding feature selection supervised learning semi-supervised learning image classification
Type de document :
Communication dans un congrès
Liste complète des métadonnées
https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02877884
Contributeur : Le2i - Université de Bourgogne Connectez-vous pour contacter le contributeur
Soumis le : lundi 22 juin 2020 - 17:41:37
Dernière modification le : mercredi 3 novembre 2021 - 04:42:35

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UNIV-BOURGOGNE | UNIV-BM | LE2I | UNIV-BM-THESE | CNRS | ARTS-ET-METIERS-SCIENCES-ET-TECHNOLOGIES | UNIV-HESAM

Citation

Ruifeng Zhu, Fadi Dornaika, Yassine Ruichek. Joint graph based embedding and feature weighting for image classification. 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), Jul 2019, Budapest, Hungary. pp.458-469, ⟨10.1016/j.patcog.2019.05.004⟩. ⟨hal-02877884⟩

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