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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2018

29.01.2018

Performance Analysis for SVM Combining with Metric Learning

verfasst von: Lingfang Hu, Juan Hu, Zhen Ye, Chaomin Shen, Yaxin Peng

Erschienen in: Neural Processing Letters | Ausgabe 3/2018

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Abstract

This paper analyses the performance of combining Support Vector Machines (SVMs) and metric learning, in order to evaluate the effect of metric learning on improving SVM. First, we establish the sufficient condition under which the performance of SVM cannot be improved by metric learning. Second, to verify whether the sufficient condition holds, we develop a two-step metric learning strategy by learning an orthonormal matrix and a diagonal matrix respectively. Third, we analyze the case when the sufficient condition holds after the two-step metric learning, and therefore demonstrate the practicability of improving the accuracy of SVM. Finally, we provide some experiments, and also apply metric learning into SVM for 3D object classification and face recognition. The experimental results demonstrate the effectiveness of improving the SVM classification performance by metric learning.
Vorheriger Artikel Robust Spectral Subspace Clustering Based on Least Square Regression
Nächster Artikel View-Based 3D Model Retrieval via Multi-graph Matching

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Fußnoten
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Metadaten
Titel
Performance Analysis for SVM Combining with Metric Learning
verfasst von
Lingfang Hu
Juan Hu
Zhen Ye
Chaomin Shen
Yaxin Peng
Publikationsdatum
29.01.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9771-7

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