Tsai M.-HYeh Y.-RLee Y.-JYU-CHIANG WANG2023-06-092023-06-092013https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885711928&partnerID=40&md5=2509627a32062a7f973ca42c36b5d096https://scholars.lib.ntu.edu.tw/handle/123456789/632079In 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. © 2013; MVA Organization. All rights reserved.Classification (of information); Computer vision; Image enhancement; Matrix algebra; Support vector machines; Computation complexity; Computation time; Data representations; Decomposition technique; Image datasets; Performance guarantees; Selection techniques; Training and testing; Image classificationconference paper2-s2.0-84885711928