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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
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02877884
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : lundi 22 juin 2020 - 17:41:37
Dernière modification le : vendredi 17 juillet 2020 - 14:59:14

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

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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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