Solving nonlinear svm in linear time? A nyström zpproximated svm with applications to image classification

Ming Hen Tsai, Yi Ren Yeh, Yuh-Jye Lee, Yu Chiang Frank Wang

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

In this paper, we improve the efficiency of kernelized support vector machine (SVM) for image classification using linearized kernel data representation. Inspired by Nyström approximation, we propose a decomposition technique for converting the kernel data matrix into an approximated primal form. This allows us to apply the approximated kernelized data in the primal form of linear SVMs, and achieve comparable recognition performance as nonlinear SVMs do. Several benefits can be observed for our proposed method. First, we advance basis matrix selection for decomposing our proposed approximation, which can be viewed as fea-ture/instance selection with performance guarantees. More importantly, the proposed selection technique significantly reduces the computation complexity for both training and testing. Therefore, the resulting computation time is comparable to that of linear SVMs. Experiments on two benchmark image datasets will support the use of our approach for solving the tasks of image classification.

原文English
主出版物標題Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013
發行者MVA Organization
頁面5-8
頁數4
ISBN(列印)9784901122139
出版狀態Published - 20 5月 2013
事件13th IAPR International Conference on Machine Vision Applications, MVA 2013 - Kyoto, Japan
持續時間: 20 5月 201323 5月 2013

出版系列

名字Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013

Conference

Conference13th IAPR International Conference on Machine Vision Applications, MVA 2013
國家/地區Japan
城市Kyoto
期間20/05/1323/05/13

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