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27.12.2019

Robust SVM with adaptive graph learning

verfasst von: Rongyao Hu, Xiaofeng Zhu, Yonghua Zhu, Jiangzhang Gan

Erschienen in: World Wide Web | Ausgabe 3/2020

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Abstract

Support Vector Machine (SVM) has been widely applied in real application due to its efficient performance in the classification task so that a large number of SVM methods have been proposed. In this paper, we present a novel SVM method by taking the dynamic graph learning and the self-paced learning into account. To do this, we propose utilizing self-paced learning to assign important samples with large weights, learning a transformation matrix for conducting feature selection to remove redundant features, and learning a graph matrix from the low-dimensional data of original data to preserve the data structure. As a consequence, both the important samples and the useful features are used to select support vectors in the SVM framework. Experimental analysis on four synthetic and sixteen benchmark data sets demonstrated that our method outperformed state-of-the-art methods in terms of both binary classification and multi-class classification tasks.

Vorheriger Artikel Extractive convolutional adversarial networks for network embedding

Nächster Artikel Spectral clustering via half-quadratic optimization

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Titel: Robust SVM with adaptive graph learning
verfasst von: Rongyao Hu
Xiaofeng Zhu
Yonghua Zhu
Jiangzhang Gan
Publikationsdatum: 27.12.2019
Verlag: Springer US
Erschienen in: World Wide Web / Ausgabe 3/2020
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-019-00766-x

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